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Polysemous on the contrary: Pragmatic theory, diachronic corpus analysis and
pedagogical approach
Timothy M. Nall
July 2014
Polysemous on the contrary: Pragmatic theory, diachronic corpus analysis and pedagogical approach by Timothy M. Nall
First published by Bookman Books, Ltd.3F, 60, Roosevelt Rd. Sec. 4, Taipei 100, Taiwan
Copyright © 2014 by Timothy M. NallAll rights reserved.
出版者╱書林出版有限公司 100台北市羅斯福路四段 60號三樓 Tel: (02) 2368-4938 FAX: (02)2368-8929發行人╱蘇正隆登記證╱局版臺業字第一八三一號I S B N╱(ISBN 978-957-445-591-1)
2014年七月一版
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Dedication
T O M Y W I F E , A N D M Y S O N :
S H U P I N N A L L & D Y L A N T H O M A S N A L L
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Preface
For more than three decades the rhetorical devices referred to as discourse
markers or discourse particles have been the subject of extensive research. This analysis
of the particle on the contrary (OTC) relies on two centuries of data from the Corpus of
Historical American English (COHA), which contains more than 400 million words of
text drawn from American English sources (Davies, 2010), rather than native speaker
intuition (Fischer, 2006b). It addresses an oft-cited shortcoming of previous discussions
by presenting a multidimensional, cross-disciplinary approach that pairs corpus data with
a fully-developed and multifaceted theoretical account drawing on primary metaphors
(Lakoff & Johnson, 1999) and Peircean semiotics (Hansen, 2006; Peirce, 1932). It also
utilizes a statistical modeling technique that is well-accepted in other fields but
innovative within Applied Linguistics: innovation accounting via vector autoregression
(Sims, 1980). It asserts that OTC is polysemous, in stark contrast to the monosemous
definition offered in many reference works and ESL textbooks. This offers an improved
account of how sentential placement interacts with language system principles to produce
different readings of OTC. It considers the impact of linguistic background on OTC use,
examines the historic decline of two meanings of OTC, and considers its diachronic
impact on the development of related structures. Finally, this analysis offers pedagogical
advice in a compact, user-friendly format.
The cross-disciplinary nature of this task cannot be overstated: my previous career
as a computer programmer enabled me to perform the extensive and heavy Python
programming that was required in the compilation and analysis of statistics regarding the
syntax and semantic content of approximately 32,000 lines of corpus data (approximate
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totals: 8,100 tokens of on the contrary; 3,860 of in contrast/by contrast; 6,160 of on the
other hand; 2,100 of sentence-initial however and 11,870 of in fact) and the interlacing of
this data with statistics generated by hansl scripts and gretl software (Cottrell &
Lucchetti, 2014), as well as gathering the biographical information of more than 5,500
authors to cull their linguistic background, transforming the data into useable format and
again utilizing that as input into the previously-mentioned sources. Knowledge of
Applied Linguistics was needed for the theoretical framework of the paper, as well as
line-by-line manual analysis of every token of OTC. Knowledge of time series analysis
was necessary for creating vector autoregression models, checking for orthogonality and
stationarity of data and so on. Corpus-based data, extensive Python programming, a
statistical approach that is innovative within Applied Linguistics and a robust theoretical
framework thus mutually reinforce one another in one tightly interlaced account coupled
with pedagogical aid, in an approach that is unique in the literature regarding on the
contrary. This analysis presents a finely-grained definition of each variety of OTC with
reference to rhetorical goals and a powerful correlation within corpus data between
meaning and syntactic form. Corpus data also indicates that Contrastive OTC is still a
viable form, particularly in U.K. usage. Data regarding syntactic form and diachronic
usage as well as a new form of Behavioral Profiles (Gries, 2010b; Hanks, 1996)
generated from forecast error variance decomposition (FEVD) analysis strongly support
the assertion that Corrective and Contrastive OTC are separate senses of one polysemous
OTC, and provide new insights into the diachronic interaction between OTC and related
discourse particles.
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Social and technological changes within a culture provoke language change (see
for example the discussion of Figure 5 on page 88, regarding a steep drop in use of one
variety of on the contrary subsequent to the 1960s). Language change ultimately leads to
changes in traditional pedagogy. Tension between the competing demands of relatively
dynamic versus conservative forces in the linguistic sphere has led to many an uncertain
moment in the classroom, echoing Silverstein’s (2006) mild but telling complaint that
navigating the tension between the demands of social theory and those of theoretical or
prescriptive linguistics is akin to “the same feeling one has in that sitcom situation of
standing one foot on the dock and another in the boat as the tide rushes away from the
shore” (p. 275). Unfortunately, the linguistic universe is not always orderly or neat; there
are few if any compact or easy answers. I do finally hope that this investigation of corpus
data will help teachers refine their explanation of on the contrary to reflect actual usage
rather than the over-simplified explanations typically offered in EFL/ESL textbooks and
other reference sources.
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Acknowledgments
Both personally and professionally, I am deeply indebted to National Quemoy
University in Kinmen, Taiwan for fostering a supportive atmosphere for academic
excellence and growth. On a purely personal level, I owe my deepest gratitude to my
beautiful wife, Shu Pin Nall, who loves me unconditionally and has been my biggest
source of support in various ways during the process of the creation of this book. Many
thanks also to Brian Goff and especially Jim Jones for their generous and timely advice,
as well as the good people of the gretl user’s list and Mark Davies for their cheerful
support as I explored hansl, gretl and the COHA corpus, respectively. Finally, I want to
thank Dylan for his encouragement, and for letting me hold onto some of his toys during
more stressful moments of the writing process. Thank you. I feel better.
vii
Table of ContentsPreface ivList of Tables......................................................................................................................ixList of Figures.....................................................................................................................xi1. Introduction & rationale..............................................................................................12. Literature review – polysemy of OTC.........................................................................92.1. Corrective OTC and reprise assertion........................................................................142.2. Contrastive OTC and intuition versus corpus data....................................................212.3. Alternative OTC and realis versus irrealis domains.................................................312.4. Metaphoric imagery...................................................................................................352.5. Viewpoint as a mediator of polysemy.......................................................................393. Peircean cognitive-semiotic model............................................................................433.1. Peircean Ground 1 – Discussion and extended examples.........................................513.2. Polysemy and language change.................................................................................663.3. Discourse communities, genres, chronotopes, and cultural attractors.......................694. Diachronic analyses of corpus data...........................................................................754.1. Data and Methodology..............................................................................................754.2. Genre-specific trends for each variety.......................................................................864.3. Syntactic structure as a test of polysemy of OTC....................................................1014.4. Impact of U.S. versus non-U.S. varieties of English...............................................1134.5. BNC and BAWE corpus data...................................................................................1184.6. Diachronic relationship of OTC to in/by contrast...................................................1224.7. Innovation accounting as a test of polysemy of OTC: 1850–1910.........................1285. Pedagogical approach..............................................................................................1516. Summary and limitations.........................................................................................161References........................................................................................................................166Appendix A: Summary Tables, all corpora......................................................................179Appendix B: Texts from COHA corpus deleted from this analysis.................................191Appendix C: Image for teaching Corrective On the contrary.........................................192Appendix D: Worksheets for teaching On the contrary..................................................193Appendix E: Typical Python program by author (corpus data processing).....................195Appendix F: Typical hansl script by author (statistics: unit roots, FEVDs, IRFs)..........200Appendix G: Forecast error decompositions of variance (1850–1864)...........................205Appendix H: Forecast error decompositions of variance (1865–1879)...........................211Appendix I: Forecast error decompositions of variance (1880–1894)............................217Appendix J: Forecast error decompositions of variance (1895–1909)............................223Appendix K: Summary tables of FEVDS, 10th horizon, by genre...................................229Appendix L: Ranked normalized particle interactions, by genre....................................232
viii
List of Tables
Table 1: Examples of Corrective OTC..............................................................................16Table 2: Corrective OTC with rhetorical question.............................................................18Table 3: Corrective OTC with reprise assertion................................................................18Table 4: Corrective OTC with approximate negator.........................................................18Table 5: Examples of Contrastive OTC.............................................................................24Table 6: Ten most recent examples of Contrastive OTC in COHA data...........................29Table 7: Examples of Alternative OTC..............................................................................32Table 8: Structure of discourse by speakers and viewpoints.............................................40Table 9: Comparing Corrective, Contrastive and Alternative OTC...................................42Table 10: Initial set of properties of Ground 1 for OTC (all varieties)..............................52Table 11: Composition of COHA Corpus..........................................................................75Table 12: Example of post-PP Corrective OTC (COHA), NS/NNS...............................102Table 13: Examples of sentence-initial Contrastive OTC (COHA), NS/NNS................103Table 14: Examples of post-NP Corrective OTC (COHA), NS/NNS, 1940–Present.....105Table 15: Post-NP Corrective OTC by genre, 1810s–1960s..........................................106Table 16: Sentence-initial Corrective structures.............................................................107Table 17: Sentence-medial Contrastive OTC..................................................................108Table 18: Raw count of syntactic structures, all three varieties, COHA 1810–2000.......111Table 19: Nonfiction texts with most instances of Contrastive OTC, NS/NNS..............116Table 20: BAWE English speakers..................................................................................118Table 21: BNC and COHA per 10k, Corrective and Contrastive, 1980–1989................119Table 22: Decomposition of variance for Nonfiction Contrastive OTC (1895-1909).....135Table 23: Legend of abbreviations in tables in FEVD analysis.......................................136Table 24: Summary of FEVDs, 10th horizon, OTC with itself........................................140Table 25: Behavioral profile of particles in genre/OTC variety combinations................147Table 26: Summary of pedagogical descriptions.............................................................153Table 27: BNC word counts, fiction and non-fiction, written data..................................179Table 28: Total COHA Word count, by genre, by decade................................................180Table 29: Corrective, Contrastive and Alternative OTC per 10K, decennial data...........181Table 30: Corrective OTC, COHA, raw decennial data..................................................182Table 31: Contrastive OTC, COHA, raw decennial data.................................................183Table 32: Alternative OTC, COHA, raw decennial data *..............................................184Table 33: Corrective per 10K, decennial data *...............................................................185Table 34: Contrastive per 10K, decennial data*..............................................................186Table 35: Alternative per 10K, decennial data*...............................................................187Table 36: BNC and COHA per 10k, Corrective and Contrastive, 1976–1994................188Table 37: English variety of authors, totals (Fiction)......................................................189Table 38: English variety of authors, totals (Non-fiction)...............................................190Table 39: COHA texts excluded from analysis................................................................191Table 40: Decomposition of variance for FICCont (1851-1864)....................................205Table 41: Decomposition of variance for FICCorr (1851-1864).....................................206Table 42: Decomposition of variance for MAGCont (1851-1864).................................207Table 43: Decomposition of variance for MAGCorr (1851-1864)..................................208Table 44: Decomposition of variance for NFCont (1851-1864)......................................209
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List of Tables (continued)
Table 45: Decomposition of variance for NFCorr (1851-1864)......................................210Table 46: Decomposition of variance for FICCont (1865-1879).....................................211Table 47: Decomposition of variance for FICCorr (1865-1879).....................................212Table 48: Decomposition of variance for MAGCont (1865-1879).................................213Table 49: Decomposition of variance for MAGCorr (1865-1879)..................................214Table 50: Decomposition of variance for NFCont (1865-1879)......................................215Table 51: Decomposition of variance for NFCorr (1865-1879)......................................216Table 52: Decomposition of variance for FICCont (1880 – 1894)..................................217Table 53: Decomposition of variance for FICCorr (1880-1894).....................................218Table 54: Decomposition of variance for MAGCont (1880-1894).................................219Table 55: Decomposition of variance for MAGCorr (1880-1894)..................................220Table 56: Decomposition of variance for NFCont (1880-1894)......................................221Table 57: Decomposition of variance for NFCorr (1880-1894)......................................222Table 58: Decomposition of variance for FICCont (1895 – 1909)..................................223Table 59: Decomposition of variance for FICCorr (1895-1909).....................................224Table 60: Decomposition of variance for MAGCont (1895-1909).................................225Table 61: Decomposition of variance for MAGCorr (1895-1909)..................................226Table 62: Decomposition of variance for NFCont (1895-1909)......................................227Table 63: Decomposition of variance for NFCorr (1895-1909)......................................228Table 64: Summary of FEVDs, 10th horizon, Fiction genre............................................229Table 65: Summary of FEVDs, 10th horizon, Magazine genre........................................230Table 66: Summary of FEVDs, 10th horizon, Nonfiction genre......................................231Table 67: Normalized particle interactions, Fiction genre, Contrastive OTC.................232Table 68: Normalized particle interactions, Fiction genre, Corrective OTC...................232Table 69: Normalized particle interactions, Magazine genre, Contrastive OTC.............232Table 70: Normalized particle interactions, Magazine genre, Corrective OTC..............232Table 71: Normalized particle interactions, Nonfiction genre, Contrastive OTC...........232Table 72: Normalized particle interactions, Nonfiction genre, Corrective OTC.............232
x
List of Figures
Figure 1: Corrective OTC. Diametrically opposed sides of a coin or disc........................37Figure 2: Contrastive OTC. Distant, opposite points across a river boundary..................38Figure 3: Corrective, Contrastive and Alternative per 10k, all genres..............................86Figure 4: Corrective OTC, four genres, 1820s–2000s.......................................................87Figure 5: Nonfiction Contrastive OTC, NS/NNS per 10k words, 1880s-1990s...............89Figure 6: Corrective OTC, Magazine and News, 1820s–2000s........................................91Figure 7: Corrective OTC per 10k, NF & MAG...............................................................92Figure 8: Corrective OTC, NF & News per 10k...............................................................92Figure 9: Alternative OTC per 10k, decennial data, 1820s–2000s....................................93Figure 10: Alternative OTC by genre................................................................................94Figure 11: Nonfiction genre, Corrective versus Contrastive OTC, 1820s-2000s..............96Figure 12: Fiction genre, Corrective versus Contrastive OTC, 1820s-2000s....................97Figure 13: News genre, Corrective versus Contrastive OTC, 1860s-2000s......................97Figure 14: Magazine genre, Corrective versus Contrastive OTC, 1820s-2000s...............98Figure 15: Contrastive OTC per 10k words, MAG & NF, 1820s–2000s..........................99Figure 16: Post-NP Corrective OTC, COHA 1810-2000 (raw data)...............................104Figure 17: Nonfiction Corrective OTC, 5 most common syntactic structures per 10k...112Figure 18: Native/non-native speakers, Contrastive OTC, Non-fiction..........................115Figure 19: Native/non-native per 10k, Corrective NF.....................................................117Figure 20: BNC Corrective & Contrastive per 10k, 1976–1994.....................................119Figure 21: BNC & COHA Corrective per 10k words, 1976–1994..................................120Figure 22: BNC & COHA Contrastive per 10k words, 1980–1989................................121Figure 23: Contrastive OTC, clause-initial In/by contrast 1830s–2000s........................126Figure 24: FEVD, Nonfiction (Contrastive OTC) 1895-1909, lag order 1.....................136Figure 25: FEVD, Fiction (Contrastive OTC) 1880-1894, lag order 1...........................142Figure 26: Fiction genre, Corrective versus Contrastive OTC, 1820s-2000s..................143Figure 27: Image for teaching Corrective On the contrary.............................................192
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1. Introduction & rationale
Over the decades since initial major works such as Halliday and Hasan's (1976) Cohesion
in English and Schiffrin's (1987) Discourse Markers, the rhetorical devices referred to in this
study as discourse particles – but frequently described elsewhere as discourse markers, among
several other terms1 – have been the subject of extensive research, offering significant space for
descriptive and theoretical linguistic analysis. Discourse particles in general offer a combination
of syntactic flexibility and a polyfunctional set of discourse uses that present both opportunities
and obstacles for researchers and teachers (Foolen, 1991, 1996; Foolen & Van der Wouden,
2002). One member of this category in particular, however, has also posed a challenge for
teachers and learners of English as a second language: the discourse particle on the contrary
(OTC). Understanding the origins of the confusion over the definitions of this term, as well as
the true state of current usage, are both fundamental academic tasks. Both are pursued here in
order to contribute to effective research and pedagogy.
Among the interesting research questions opened by OTC are: whether discourse particles
are truly polysemous or have but one core meaning and peripheral uses, the relationship between
rhetorical function and syntactic form in written discourse, the dynamics of metaphoric extension
of a locative expression into a metatextual discourse particle (through “pragmaticalization”, see
Aijmer, 2002), the historic decline in the use of one form of the particle, the diachronic impact
that decline has had on the development of related structures or in variations in discourse particle
use across discourse communities and native speaker groups, and the most effective statistical
method for analyzing the dynamic, fluid relationships between discourse particles and other
1 The terminological distinction between “discourse marker” and “discourse particle” is a meaningful one, as will be discussed. For theoretical reasons, this present analysis will prefer “discourse particle.” However, quoted text will of course preserve terminology as it appears in the original sources (e.g., “discourse marker” or “DM”), as will discussion of such texts.
1
rhetorical elements (including other discourse particles). Given the number of issues that need to
be addressed and the complexity and polyfunctionality of these particles, a refined analysis
requires the adoption of a multidimensional, cross-disciplinary approach (Schwenter, 1999, p.
245). To this end, this analysis offers a more concrete and detailed definition of OTC than has
been presented in other research, as well as an in-depth exploration of an extended set of issues
that arise after defining the particle.
One original aspect of this research is that it offers an analysis tailor-made to answer
Bell’s (1998) telling complaint that current accounts of discourse markers or particles “fail to
address the actual properties of each marker and how these properties interact with particular
contexts in order to produce these different readings” (p. 518). This analysis offers a cross-
disciplinary account that targets Bell’s complaint and answers it in full. First, a new and in-depth
explication of the discourses uses of OTC is presented, based on U.S. English data from the
Corpus of Historical American English (COHA) (Davies, 2010).2 That data is decomposed into
four genres (Fiction, Nonfiction, News and Magazines) to analyze of genre upon discourse uses
of OTC. A smaller subset of relatively synchronic British English usage data from the British
National Corpus (BNC)3 and synchronic data from the corpus of British Academic Written
English (BAWE) (Gardner & Nesi, 2012)4 is also examined in the context of data regarding the
variety of English spoken by more than 5,500 individual authors whose texts are incorporated
into the COHA corpus, derived from extension research of their biographical information. This
2 Unless otherwise specified, most illustrative examples given here are from the Corpus of Historical American English (COHA), and most quantitative analyses refer to COHA data. To save space, COHA examples are not specifically cited as such. Examples from other sources are as cited.3 Some data cited herein has been extracted from the British National Corpus Online service, managed by Oxford University Computing Services on behalf of the BNC Consortium. All rights in the texts cited are reserved.4 Some data in this study comes from the British Academic Written English (BAWE) corpus, which was developed at the Universities of Warwick, Reading and Oxford Brookes under the directorship of Hilary Nesi and Sheena Gardner (formerly of the Centre for Applied Linguistics [previously called CELTE], Warwick), Paul Thompson (Department of Applied Linguistics, Reading) and Paul Wickens (Westminster Institute of Education, Oxford Brookes), with funding from the ESRC (RES-000-23-0800).
2
analysis also examines the interface between syntax and semantics by noting the correspondence
between sentential placement and semantic import. Finally, an especially direct and compelling
answer to Bell’s complaint is offered via a new form of Behavioral Profiles (Gries, 2010; Hanks,
1996) generated from forecast error variance decomposition (FEVD) analysis. These directly
analyze the interaction between discourse particles in context for the 60-year period from 1850
through 1910 for three written genres (Fiction, Nonfiction and Magazine). This statistical
approach, although well-established in other academic disciplines (particularly Macroeconomics)
is to the best of my knowledge innovative within the disciplines of Corpus Linguistics and (by
extension) Applied Linguistics.
Another benefit of this cross-disciplinary approach also derives from its extensive
application of Corpus Linguistics techniques. Fischer (2006b) suggests that results from corpus
data are preferable to relying on native speaker intuition in the analysis of discourse particles:
… it is assumed that intuition is not reliable if we want to determine the meanings and
functions of discourse particles. Instead, it is necessary to base each analysis on the close
examination of corpora… (p. 428)
Fischer goes on to note an apt comment by Sacks (1984) that corpus data is also preferable to
largely decontextualized, invented examples, since “from close looking at the world we can find
things that we could not, by imagination, assert were there” (p. 25). That is, appeals to native
speaker intuition and even established reference works may not always be the most productive
approach to analyzing discourse particles, whether they are considered across native speaker
groups, or diachronically within a given discourse community or community of practice. As
Aijmer (2002) points out, “the dictionary is of little help … we need to check on how well a
suggested core definition explains the data in a corpus” (p. 23). Corpus data can supply the
3
necessary set of contexts to examine discourse particles’ usage more completely and accurately,
since such particles cannot be examined fully independent of context (Levinson, 1983, p. 33).
These observations motivate a desire to appeal to corpus data within this study, in explorations of
syntactic and other behavior, and to verify the behavior of discourse particles in relation with one
another via innovation accounting, in a new approach to the data offered in this analysis.
However, corpus data and statistical analyses do not tell the whole story. The data should
optimally be intertwined with a theoretical explanation (Gries, 2010a), as Bolinger (1989) states:
Studying distribution is a good procedure for discovering meaning, provided something is
already known about the meanings of the items distributed; but accounting for the
distribution of meaningful items presupposes some theory of what the items
mean. (p. 301)
This present analysis is also designed in part to answer the call by Bolinger and Gries for corpus
data to be matched with a robust theoretical account. This is accomplished via the cognitive-
semiotic model of Hansen (2006) which draws on Peircean semiotics (Peirce, 1932) as its
framework of origin. The model is both defined and extended in a process tightly linking data to
theory. The model and the cognitive-semiotic theory are then paired with another layer of theory,
in which the metaphoric origins of the discourse particle are discussed in section 1.3, beginning
on page 34. Two separate and distinct (but closely related) instances of metaphoric imagery are
described. Taken together, these images comprise a native and integral element of the cognitive-
semiotic model. Moreover, far from being an unsupported theoretical departure from the real
data, the metaphorical elements are also tightly connected to the robust empirical correspondence
between the semantic import of each of three varieties of OTC and the syntactic environment
where they typically occur (as described in section 4.3 on page 101). All three elements briefly
4
discussed thus far – corpus-based data, statistical analysis and a two-layered (cognitive-semiotic
and metaphorical) theoretical modeling approach – thus mutually reinforce one another in one
tightly interlaced account.
Based on corpus findings buttressed by the theoretical framework offered in Hansen
(2006), this analysis will offer an account that among other things will contend that on the
contrary presents a case of polysemy. It will describe three separate varieties of OTC in
discourse: Corrective, Contrastive and Alternative. Of all three forms, the Corrective type is
unquestionably the most strongly attested in the COHA corpus data, and is the default structure
for both U.S. and British usage. Contrastive OTC is still in minority usage – rather weakly so in
U.S. usage, though its use is better represented in the U.K. Alternative OTC is not defunct, but it
is certainly the least well-represented among the forms.
Finally, discourse particles have a long and somewhat negative history of engendering
pedagogical discussion, much of it focused on warnings against their misuse. As instructors of
English as a foreign language may attest, the discourse particle OTC presents a nontrivial set of
challenges for second language learners. Indeed, repeated encounters with the specific scenario
in which students seem to confuse on the contrary with on the other hand lead one experienced
ESL educator to opine that “[in] my experience as an EAP teacher this is one of the most
consistent misapplications of a written lexical unit” (Lake 2004, p. 137). Students insert OTC
into their papers in ways that teachers do not consider acceptable or well-formed. Teachers then
correct those papers in a manner that may or may not be consistent with real-world usage.
Dictionaries and pedagogical texts are also somewhat unhelpful, offering advice about OTC that
is at odds with real use (see discussion in Lenk, 1998).
5
Certainly from a pedagogical perspective, this level of attention in the literature does not
seem unreasonable. One reason for the emphasis on discourse particles may be that they are
considered a necessary tool in academic writing, since written academic discourse has a
significantly higher usage rate of contrastive discourse particles than would other genres, and
since comparative discourse markers occur only rarely in spoken discourse (Povolná, 2009). For
example, Bell notes with respect to one variety of on the other hand, “the major occurrence of
[its use] is in discourse genres such as academic journals, where the rhetorical function of the
comparison and contrasting of items is an essential element of the discourse” (Bell, 2004, p.
2179). A second reason is that studies have shown that even discourse particles whose
decontextualized interpretation(s) appear to overlap cannot be used interchangeably (e.g. Fox
Tree & Schrock, 2002). For example, Tseng (2013) has shown that when Taiwanese university
students and faculty are offered a chance to replace missing connectives removed from academic
discourse written in Mandarin Chinese by choosing from a small set of options, some
respondents (especially the students) insert the wrong connectives into the wrong positions,
significantly changing the document’s overall meaning. The communicative value of discourse
particles is therefore clearly important enough to warrant attention from English language
teachers. In line with a key goal within the field of Applied Linguistics to merge descriptive data
with prescriptive teaching, a further key objective of this analysis is to create solid pedagogical
prescriptions based on extensive corpus data. These are presented in Section 5, “Pedagogical
Approach”, beginning on page 151.
This present analysis, grounded in cognitive-semiotic theory and supported by sound
statistical techniques that examine two centuries of U.S. English corpus data, offers a fresh
combination of a theoretical approach matched with corpus data analysis and pedagogical
6
application, to explore use in context. The multiple aims of this cross-disciplinary analysis
include the following:
Presenting a finely-grained definition of OTC and explication of its syntax, with
reference to corpus data, and within a well-motivated theoretical framework
exploring the polysemous nature of OTC
utilizing diachronic usage rates, distinctive syntactic structures and the results of
forecast error variance decomposition (FEVD) analysis to support the assertion that
Corrective and Contrastive OTC are separate meanings of one polysemous discourse
particle
connecting the diachronic development and change of metatextual OTC to a well-
motivated theory
drawing conclusions principally from corpus data rather than from native speaker
intuition or reference texts
depicting historic trends that have led to the rise or fall in use of this and related
discourse particles and rhetorical devices, and made the use of OTC problematic for
second language teachers and learners
describing the metaphoric origins posited for OTC
addressing issues of genre as well as U.S. English versus non-U.S. English speaker
use in published materials.
examining the syntactic forms of different varieties of OTC and their connection to
each variety’s rhetorical goals and semantic content
incorporating corpus data into an explication of the sentential placement of OTC, and
its relation to the different possible meanings of OTC in context, into a pedagogical
approach
The expectation is that this multilayered analysis will add to the corpus-driven, usage-based
cognitive-linguistic research regarding discourse particles, trace the connection between historic
data and cognitive-semiotic theory with respect to both synchronic and diachronic development
and use, and help teachers refine their explanation of on the contrary to reflect actual usage
7
rather than the over-simplified explanations typically offered in EFL/ESL textbooks and other
reference sources. Although the more finely-grained details of this explanation may be more
suitable for advanced learners, teachers certainly should be prepared to raise intermediate
learners’ awareness that there is more than one syntactic form, and perhaps also that there is
more than one meaning.
8
2. Literature review – polysemy of OTC
Discourse particles (or markers) as a category have been the subject of considerable
descriptive studies in general linguistics for the past few decades, as for example in Wagner’s
(2011) examination of although, however, nevertheless and while across six different corpora.
Degand (2009) notes an “explosion of empirical and theoretical research into [discourse markers]
in the last 20 years” (p. 173). And as Pons Bordería (2005, p. 93) points out, these particles in
general offer a singular amount of space for both descriptive and theoretical research, in a “close
and fertile” relationship between these rhetorical devices and pragmatic theory. The fields that
Pons Bordería (2005) describes as being explored with respect to discourse markers include:
“Text Linguistics (e.g., van Dijk, 1977), Relevance Theory (Blakemore, 1987), Argumentation
Theory, Gricean Pragmatics, grammaticalization and intersubjectification (Traugott, 1993, 2003)
and other areas” (p. 93). In general, discourse particles have presented a complex challenge for
most theories within the umbrella category of pragmatics.
Perhaps the most common account of the discourse particle on the contrary (OTC) is that
it functions as a discourse marker (e.g. Blakemore, 2002; Fraser, 1999; Halliday and Hasan,
1976; Levinson, 1983; Risselada & Spooren, 1998; Schiffrin, 1987; Schourup, 1999; Stubbs,
1983). In the “discourse marker” account, conjuncts such as OTC make no contribution
whatsoever to the truth-conditional status of the two propositions, and merely indicate the
manner in which the relationship between the two segments is intended to be understood in
context (Connor, 1996; Fraser, 1990, 1996; Greenbaum & Quirk, 1990; Östman, 1982). Accounts
which address the discourse roles of OTC within the “discourse marker” framework hold that it
is placed between two propositions, P1 and P2 as a signal to help guide the reader/listener into an
understanding that the semantic frame being presented in P2 is one of opposition, and that the
9
categorical distinctions drawn between P1 and P2 are so strong and clear (at least within the
subjective opinion of the speaker/writer) that the two are felt to be in some sense in opposition to
or quite the reverse of one another (Fraser 2009b, pp. 87–88). The distinction that has been
drawn in this analysis between semiotic content and semantic constraints maps neatly onto
Blakemore’s (1987) relevance-theoretic distinction between encoded procedural and conceptual
information. In fact, it has been argued that conceptual and procedural features can coexist
within a single marker (Pons Bordería, 2008). The distinction between the two types of content is
quite relevant: conceptual information comprises the core content of a text or utterance and bears
its truth-value, while procedural information simply facilitates the hearer/reader’s understanding
by providing constraints upon the set of possible interpretations, for example by placing it within
a framework such as speaker and/or hearer expectations. Thus in the “discourse marker” account,
if OTC contributes any conceptual content at all, it does so only as an essentially monosemous
structure that may have “core” and “peripheral” uses or senses (Bell, 1998).
Discourse particles are also frequently described as “polyfunctional” (Foolen, 1991;
Fraser, 2009a; Hummel, 2012). The polyfunctional nature of discourse particles in general and
OTC as a particular member of the category is reflected in prior literature by extensive and
substantial disagreement as to the definition and terminology regarding this category of
rhetorical device, as well as by the placement of OTC in a number of different classificatory
schemes. Several sources discuss this terminological disagreement, among which Brinton (1996)
and Schourup (1999) are representative cases. As for on the contrary in particular, the lack of a
unified account of its nature suggests that there is still significant room for analysis. For
example, in the classificatory system of Halliday and Hasan (1976), on the contrary is placed
within the category of correctives, members of which may be paraphrased as ‘not X but Y’. In
10
that taxonomy, moreover, both OTC and in/by contrast would also be adversative. In Quirk et. al.
(1985, p. 631), which draws on the earlier work of Greenbaum (1969), OTC falls within the
antithetic conjuncts, which suggest a “direct antithesis” within a contrastive relationship. Finally,
in Borkin (1979, p. 45) OTC is explicitly placed in the category OPPOSITION IN
REPLACEMENT. Her further discussion of OTC suggests a framework that would also place
the less-frequent Contrastive form beside in contrast and by contrast in her OPPOSITION IN
COMPARISON category, though she adds that this sense of OTC “expresses even more direct
opposition” than the latter two conjuncts (1979, p. 47). In each of these classificatory schemes,
these broad functional categories are expressly intended by their respective authors as a starting
point rather than a fine-grained explication. Nevertheless, Bell (1998) takes issue with Halliday
and Hasan and Quirk et. al. to some degree, suggesting that these classificatory schemes can
easily be misunderstood as creating an undesirable implication of polysemy, which may further
cloud our understanding of discourse markers rather than clarifying it (Bell, 1998):
The question is whether... these different classificatory schemes ultimately illuminate or
obfuscate the phenomenon... this kind of multi-functional categorization does fail to address
the actual properties of each marker and how these properties interact with particular
contexts in order to produce these different readings. (p. 518)
Blakemore (2002) goes one step further by asserting that the inability of the field of linguistics to
place discourse markers within a unified or coherent categorization scheme implies that
discussion of the group as a whole is unprofitable, although research into the properties of
individual DMs may still yield some insights (p. 185). Not only is there great disagreement
among scholars as to the intrinsic nature of discourse particles, but Blakemore’s account adds
sharp disagreement as to the theoretical value of analyzing them as a distinct class.
11
In contrast with Bell (1998), Fraser (1999) and some authoritative reference works, but in
line with other researchers (notably those found within Fischer, 2006a), the semantics of OTC
are analyzed here as a case of polysemy. More specifically, the discourse particle OTC is a rather
complex (but underspecified) radial network of two discourse devices: images and constraints.
On this view, OTC presents separate definitions derived from separate metaphoric extensions of
its core origin in a locative prepositional phrase. Relatively recent research (e.g. the contributions
in Fischer, 2006a, as well as Fischer, 2000; Foolen, 2006; Travis, 2005) has begun to explore
discourse particles as polysemous, as Hummel (2012) states:
In studies on discourse markers, polysemy was long neglected, since the scientific focus
was onomasiologically applied to possibly universal… discourse functions such as
reformulation, introduction, elaboration, acceptance, closing, etc. Recent research has
semasiologically insisted on polysemy. (p. 2)
This analysis shares in the recent trend of reconsidering discourse particles with a polysemous
framework.
The shared goal of Corrective and Contrastive OTC is to make boundaries abundantly and
emphatically clear in a given context. Both bear a significant level of emphatic force: in both
varieties, given two propositions P1 and P2, OTC is a rhetorical device employed when the
speaker/writer chooses to close off the possibility or presumption that either the referent or the
predicate (or both) of P2 could be mistaken for its counterpart in P1. These two varieties of OTC
thus insert an adamant, uncompromising rhetorical wedge between some key qualitative element
of a proposition P1 that was made in prior discourse and a projected (and almost certainly
forthcoming) discourse-new P2. OTC completely separates the two propositions and shears away
any assumption of any qualitative overlap between them with respect to their referent or
12
referents, the predicated natures of those referents, or both. To employ a military metaphor,
whereas some discourse particles may be used rhetorically as a defensive parry (i.e., a hedging
device) or a cautious probe, as for example employing on the other hand to offer a potentially
plausible alternative explanation for some phenomena, an instance of Corrective or Contrastive
OTC is invariably an emphatic assertion that is intended to administer a rhetorical coup de grâce
to any perception that P1 and P2 can be considered near complements within some specified
context-relevant domain.
The semantic content of OTC in its role as a discourse particle bears antithetical sense and
meaning that are intuitively accessible by appeal to the Latin root of the word “contrary”
(contrarius “opposite, opposed”), and indeed to the present English definition of “contrary.” Not
by coincidence, the same Latin root contra is found in the word “contrast.”5 The sense of
opposition within the Latin root is carried directly into English, first in its use within a
prepositional construction with locative import and later by metaphoric extension into discourse
particles. In all three varieties of OTC, OTC cannot properly be employed unless it is located
between two discourse elements that the speaker/writer chooses to frame as a categorical
distinction, so its presence immediately signals that such a categorical distinction can and will be
drawn.
In general, because the two semantic goals – correcting one idea about one referent
versus comparing two referents – are not interchangeable, the two main varieties are separate and
distinct. Corrective schemas can only rarely be recast into Contrastive ones. Although Alternative
OTC might properly be seen as a subset of Contrastive based on the close match between their
5 Webster’s 1913 dictionary defines contra as “A Latin adverb and preposition, signifying against, contrary, in opposition, etc., entering as a prefix into the composition of many English words”, retrieved from http://machaut.uchicago.edu/?resource=Webster%27s&word=Contra&use1913. The Cambridge online dictionary, in both British and American English, defines contrary as “opposite.” Its American English definition of on the contrary is given as “actually, the opposite is true.” See http://dictionary.cambridge.org/dictionary/american-english/on-the-contrary
13
semantic schemas, the presence of only one referent in Alternative, as well as syntactic
differences and significantly different levels of usage between the two in the COHA corpus data,
has led this analysis to treat them separately. These varieties are discussed in the next three
sections.
2.1. Corrective OTC and reprise assertion
In the semantic schema that characterizes the Corrective variety, which is the default
structure for both U.S. and British usage, the element of P1 that is being rejected is its truth-value
– that is, the validity of the asserted relationship between its referent and predicate. However,
although the conceptual content that OTC contributes to discourse is relatively transparent, the
prominence of this intuitively accessible processual profile can contribute to an incomplete
understanding of the available range of OTC’s senses and meanings. Perhaps as a result of
adhering too closely to this readily available path to understanding (and perhaps also as a
reflection of regional variations in use), a common pedagogical approach regarding the definition
of OTC involves taking the position that the only acceptable use of OTC is as a conjunct that
frames a corrective – and only a corrective – relationship between the two propositions P1 and
P2 (see for example Fraser, 2009b).
In the Corrective variety of OTC, the truth value of the first assertion P1 is denied by OTC
itself, and the second premise P2 supports and enriches that denial by realigning one or more
elements of P1 with an (asserted) alternate version of reality. When this rhetorical wedge is
inserted between propositions sharing the same referent, its syntactic characteristics and semantic
environment match perfectly with the set of characteristics that Lake (2004) offers as a means of
14
verifying whether OTC has been placed in an environment that is semantically and syntactically
appropriate:
one subject;
two contrasting qualities;
one positive statement and one negative statement open to similar interpretations;
an argument, either genuinely present or implied, to which the two statements adjacent
to the phrase both form a refutation. (p. 142)
Particularly in U.S. English, the association is so strong between OTC and its Corrective variety
that one premise is in some sense categorically in opposition to the preceding one, similar to the
earlier discussion of Langacker’s (2013, pp. 48–49) statement that one of the semantic
associations attendant on a lexical item can approach categorical status. In the typical case,
additional enriching information will be provided in the proposition P2, as well as subsequent
discourse.
At the most general level, Corrective OTC is expressed in two distinct forms: one-person
and two-person (Fraser, 2009b). The first form occurs within a conversation between more than
one speaker. Since this analysis only considers written data, the incidence of two-person OTC
tokens within the data is skewed very heavily toward dialogs within the Fiction genre, as in
example (1):
(1) “Advertising,” Hunt said thoughtfully, “is the unspeakable expression of an
unspeakable age.”
“On the contrary,” Morgan said, “I adore advertising. It is the only form
of modern art to concern itself, however remotely, with the truth.”
Similar conversations might also reasonably be found in Nonfiction works such as biographies.
The COHA data includes a few instances that are placed within the Nonfiction genre (sometimes
15
arguably), but this analysis will not examine these in greater detail. Examples of Corrective OTC
from the COHA database, showing the P1 and P2 of both one- and two-speakers forms, are shown
in Table 1:
Table 1: Examples of Corrective OTC
(2) A: “My God! Sigurt! What d' you think you're doing! You can't fly! This is not
a game!”
B: “On the contrary,” said the alien cheerfully. “The whole universe is a game, is
it not?”
(3) Unlike Ronald Reagan, he is not inherently hostile to government;
on the contrary, he is its creature.
(4) In his urgency then, Wittgenstein was hardly building an airtight argument. He was
not piercing or succinct. On the contrary, he was thoroughly scattered and
staccato.
(5) The centralized state may be little more than a tax collecting and public works
agency; there is no fundamental political integration, but, on the contrary, a strong
tendency for the separate units within the state to break away.
(6) In short, the intransigent non-payment strategy is an extremely risky and harmful
one for Iraq to pursue at a time when the country is in urgent need of fresh credit
and new openings towards the international financial market. A strategy such as
this can not succeed in elevating the burden of indebtedness. On the contrary it
could very well aggravate the problem even further.
(7) It’s not obvious that truth judgments are absolute, and it’s not obvious that so-
called conventional implicatures are irrelevant to determining the truth values
assigned to the speaker’s proposition taken as a whole. On the contrary. There is
some preliminary support for Bach’s proposal that truth evaluations are tied to
degree of prominence… (Ariel, 2010, p. 270)
16
Examples (2) through (7) (above) display various syntactic instantiations of Corrective OTC. Of
particular note are example (2), which is a fictional instance of the two speaker case, and
example (7), which shows a very recent instance of OTC employed as a syntactically stand-alone
assertion.
The proposition following OTC does not perform the cancellation of P1; both the bare
presence of OTC and the lack of veridicality in P1 framed as a “reprise assertion” structure
(useful for maintaining the epistemic shift to a “dialogical monologue” viewpoint inherent in
one-speaker Corrective OTC, see Table 8, page 40) have already competed that semantic task.
Instead, immediately subsequent discourse tends to enrich the existing cancellation by adding
further details – details that moreover align with OTC’s semantic schema of “completely
opposite.” If any additional inferences are cancelled within the discourse scope of Corrective
OTC, they are cancelled by the enriching proposition that follows rather than by OTC itself.
The most common form overall, one-party Corrective OTC, is in many respects
well-suited to the academic writing genre. It allows an individual writer to set up an initial
proposition and then contradict it with a following proposition. However, as seen in several
examples in Table 1 (above) its syntax typically requires it to follow statements that seem
incompatible with a schema that requires opposition or contrast between P1 and P2. Specifically,
one-speaker OTC must follow a proposition P1 that involves negation or entails negative
polarity. According to Swan (2005, p. 140), the speaker/writer “strengthens a negative statement
which he/she has just made.” Borkin (1979) says, “With on the contrary, what is being said is
not in complete opposition to what has been said before, but rather to what has been denied
before” (p. 54. Emphasis is Borkin’s). The two paralleled instances of negative polarity are
typically an obligatory rhetorical strategy used to manipulate speaker viewpoint via reprise
17
assertion, as discussed immediately below, as well as in section 2.2 beginning on page 39. The
negation or negative polarity in P1 may be framed either as a rhetorical question, a negative
statement (i.e., a negation), or a statement containing an approximate negator (Huddleston &
Pullum, 2002, p. 815) such as rarely, seldom, barely, hardly, scarcely, few or little, as seen for
example in (4) above, and (10) below. An example of each form of negative polarity follows:
Table 2: Corrective OTC with rhetorical question
(8) …within the next four years, one person in every eleven in this country will be killed
or injured in traffic accidents. Does this mean that you should drive or ride in fear
and trembling? On the contrary, you should realize that the relaxed driver is less
accident-prone than the supercautious creeper.
Table 3: Corrective OTC with reprise assertion
(9) The collapse of the Roman Empire in the West was not the end of civilization. On the contrary, the successive waves of barbarian peoples evoked a new kind of
civilization, more robust than the ancient polis.
Table 4: Corrective OTC with approximate negator
(10) Such a travelling man is seldom looked upon as a bore, but, on the contrary, is
welcomed by the relatively limited number of men with whom it is his mission to
establish or maintain business relations.
Each of these structures has in common a lack of veridicality. Veridicality is an assertion of the
truth of a proposition, at least to the extent to which the relevant knowledge structure accurately
reflects the information environment it represents (Walsh, Henderson, & Deighton, 1988). More
formally:
18
i. A propositional operator F is veridical iff Fp entails p: FP →p; otherwise F is nonveridical.
ii. A nonveridical operator F is antiveridical iff Fp entails not p: Fp →¬ p (Giannakidou, 2002, p. 5)
Rhetorical questions are thus nonveridical in that they have “…the illocutionary force of an
assertion of the opposite polarity from what is apparently asked” Han (2002, p. 201). Negation of
truth self-evidently distinct from the assertion of truth, thus also lacks veridicality. Finally,
approximators lack veridicality in that they “…imply a denial of the truth value of what is
denoted by the verb” (Quirk et al. 1985, p. 8.112).
The speaker/writer of one-party Corrective OTC needs to juggle two viewpoints: he or
she is speaking alone, but disagreeing with an assertion by a proponent who is not present. The
remark may dispute an assertion by an actual communicating party mentioned in prior discourse,
or may instead reject inferences readily derivable from the topic domain, in a context in which
there is reason to believe that the denied proposition is plausible (Wason, 1965). In either case,
the position being disputed must somehow be explicitly stated before OTC can properly be
employed. The transformation that prefaces one-speaker OTC – that is, from a positive assertion
by some party other than the current speaker/writer to a negative assertion by the current speaker
– is well-motivated in English and other languages. This structure simply adopts the form of a
“reprise assertion” (Godard & Marandin, 2006). According to Godard & Marandin (2006),
reprise assertions stand as a negative parallel made in response to a positive assertion of fact:
…plain assertions [commit] the speaker to a propositional content and, simultaneously, [call]
on the addressee to acknowledge that content. By uttering a reprise assertion, the speaker
makes a statement whose content is reprised from the ongoing context and which conveys
his/her distance from this content. (pp. 189–190)
19
In the case of OTC, the positive assertion is generally implicit within the context. Reprise
assertion thus serves the illocutionary purpose of denying a prior assertion by recasting it as a
negation and then repeating it, as Schwenter (2008) notes with respect to the example below, in
which the reprise assertion has been emphasized (11):
(11) ...resolvemos subir até o trampolim mais alto da piscina. “Caralho, é muito alto”,
eu disse. “É alto nada, João”, Carlinhos falou.
‘...we decided to climb up the highest diving board at the pool. “Damn, it’s very
high”, I said. “It’s not high, João”, Carlinhos said.
[http://www.screamyell.com.br/pms_cnts/tkcinco.html] (Schwenter, 2008;
Schwenter’s example 1; emphasis added)
Negation is always reactive; it never arises from a contextual vacuum, but functions instead as
both a reply and an act of opposition to some other speech act which either precedes current
discourse, or when the speaker “presupposes the possibility, if not the expectation” of such an
assertion (Israel, 2004). In a monological/dialogical context (Schwenter, 2000; 2008), given that
OTC assumes one state of reality as a background entity and then emphatically rejects the
veridicality of that entity within the current discourse space (in much the same way as all
negation does; Langacker, 1991, pp. 134–139), utilizing reprise assertion is a pragmatically well-
motivated strategy that has become conventionalized for one-speaker OTC. The communicative
goal of this rhetorical device, as will be discussed in the section regarding viewpoint as a
mediator for polysemy (section 2.2, page 39), is to simulate dialogue and thus convey the
dialogical nature of the semantic frame.
20
1.1. Contrastive OTC and intuition versus corpus data
Corrective OTC is employed when the speaker/writer wishes to reject one premise about a
single referent, and supplant that premise with another. In the case of Contrastive OTC, two
separate referents are described as being in some manner categorically distinct and poles apart,
and OTC is employed as means to highlight that key difference between them. The principle
function of Contrastive OTC is to create a contrast set relationship between two separate and
distinct items in discourse space, though the two items must share membership in some semantic
category that is also relevant to current discourse. The two items either coexist in the domain
described by discourse, or exist as irrealis possibilities or options, as in the case of Alternative
OTC (section 1.2, page 31). Crucially, whereas Corrective OTC draws a distinction between two
referent propositions and asserts that only one of the two exists or has existed (i.e., the two are
mutually exclusive), Contrastive OTC describes the case in which both items coexist. Though the
differences between these two items are typically objective and categorical, that need not always
be the case. It is sufficient that the speaker/writer perceives the two referents as categorical
opposites, perhaps in a subjective manner or perhaps strictly limited to a specific context.
As opposed to the rhetorical goals of Corrective OTC, the Contrastive variety makes no
attempt to correct or even address the truth value of the proposition P1 that it follows. Instead, it
accepts that value as given and moves on to a new discourse goal. Acting again as a rhetorical
wedge, Contrastive OTC creates a mental space for the introduction of a second referent, as well
as a new proposition P2 about that referent. By creating that mental space, moreover, OTC
functions as a contract for the delivery of such a proposition. The goal of Contrastive OTC is to
show that the referent of P2 is wholly dissimilar to that of P1, to the degree that they considered
opposites constituting a contrast set relationship. The schema that is relevant to Contrastive OTC
21
contains two referents and two corresponding references. All four are presented as co-existing
within a single frame of reference in realis setting in time and space, so none are mutually
exclusive. Drawing on corpus data, it can be seen that the boundary conditions necessary for
appropriate use of Contrastive OTC with respect to some proposition P2 are as follows:
The referent of P2 must be categorically related to that of P1 by virtue of shared
membership in some discourse-relevant group
However, the truth-value of P2’s predicate does not depend upon the truth value of P1,
and does not affect it in any manner.
The key element of P2’s nature is that its referent must be categorically distinct from
that of P1 in some salient respect, which is described in the predicate of P2. Contrastive
OTC then creates a contrast set relationship between one or more elements of P1 and
P2.
Thus the Contrastive variety of OTC evaluates P1 and P2 along some cline and contrasts two
premises, both of which are presented as accurate descriptions of reality – realis assertions that
are not mutually exclusive, but which offer information about two separate referents. The
rhetorical goal of Contrastive OTC then is to place that dissimilarity at the forefront of the
current discussion, and present it as an indisputable gap or divide between the two referents. To
do so, Contrastive OTC first invokes scalarity and then places propositions P1 and P2 at opposite
endpoints along that scale (whether one of comparison, gradation, intensification or
quantification).
Specifically, this structure constricts the discursive relationship between two propositions
in a manner that closes off all possible worlds in which P1 and P2 might be perceived as sharing
discourse-relevant similarities that would make them meaningfully compatible, synonymous or
interchangeable along any scale that sets the predicate of P1 as its base value. Thus the two
22
referents are linked together by shared membership in some discourse-relevant set, but OTC
works to create an insurmountable gap – a metaphorical “river” – that draws an indelible contrast
between the two predicates within discourse. The referents of P1 and P2, in short, are described
as wholly dissimilar members of the same discourse-relevant group, category or set. A few
characteristic examples are offered in Table 5, below:
Table 5: Examples of Contrastive OTC
(12)The sheer front is essentially plane and looks as if it had been cleft through the
body of the dome… The back, on the contrary, curves without break into the
crown and sides, its huge, wonderfully continuous shells wrapping around these
parts. How could fractures similar in trend and attitude have given rise to surfaces
wholly dissimilar in modeling?
(13)They have seen in [Stephen Douglas’s] round, jolly, fruitful face post offices, land
offices, marshalships and cabinet appointments, chargeships and foreign missions
bursting and sprouting out in wonderful exuberance ready to be laid hold of by their
greedy hands… On the contrary nobody has ever expected me [i.e., Abraham
Lincoln] to be president. In my poor, lean, lank face nobody has ever seen that any
cabbages were sprouting out.
(14)In an earlier day, when “until death do us part” was taken seriously by the majority
of people, married couples, particularly the women… often went to extremes of
misery rather than break up their homes. Today, on the contrary, the attitude
toward marriage is so casual that young people do not even allow time for the
normal, necessary adjustments before flying to the divorce courts.
(15)In the Ottoman empire the miniaturists… did not achieve quite such high distinction,
perhaps because painting was frowned on by the ulama who played so important a
role in the structure of the empire. In the Mogul empire, on the contrary, the art, at
first imported from Iran, took on a vigorous independent form.
(16)Medical illnesses, while unfortunate, are not commonly pejorative. Psychiatric
diagnoses, on the contrary, carry with them personal, legal, and social stigmas.
23
The contrasted pair of referents, their respective discourse-relevant groupings and the cline along
which they are contrasted within these examples are: a sheer, flat face versus a rounded face of
one rock structure (the two sides of the Half Dome rock formation in Yosemite, contrasted by
physical characteristics); Lincoln versus Stephens (i.e., Republican versus Democratic candidates
for U.S. President in 1858, contrasted by their proclivity to expand the offices and expenditures
of national government); past versus present behavior (married couples, contrasted by their
readiness to divorce); Ottoman art versus Mogul or Mughal art (art in Muslim Empires,
contrasted by the perceived vigor of their expression), and medical versus psychological illnesses
(maladies treated by health care professionals, contrasted by degree of social stigma for the
patients).
Finally, it seems at least possible that a given instance of OTC which upon initial
inspection appears to be Contrastive, since both of the two surrounding premises describe
conditions that are realis but not mutually exclusive, could actually be Corrective or cancellative
if it cancels an expectation arising from the first premise that should presumably be applied to
the second, as seen below in example (17), from the BAWE corpus:
(17) Also patients with cirrhosis had significantly higher concentrations of laminin in
serum than controls (p<0.0001)… No obvious relation was found between the
laminin concentrations and viral markers, cholelithiasis, ultrasonographically and
clinically evaluated portal hypertension, oesophageal varices, drug addition,
smoking habits, treatment, previous icteric, encephalopathic, ascitic or
gastrointestinal haemorrhages, aetiology, reason for admission, urinary bilirubin and
urobilinogen concentration, hepatomegaly, splenomegaly, or quantity of ascites. On
the contrary, relations were found with telangiectasis (p=0.01), spider naevae
(p=0.02), and malnutrition (p=0.02).
24
Here the topic is “relations between laminin concentrations and other medical factors.” The
medical factors listed are all either symptoms of cirrhosis of the liver, or factors presumed to be
correlated with the same. However, the assertion following OTC does not cancel any aspect of
the information from the preceding assertions. Did the author intend to cancel any expectations,
based on previous studies or background knowledge of the topic domain, would lead one to
believe that laminin should be present? This depends on the nature of the underlying premises,
which are left unstated. However, no further instances of this sort were apparent in any of the
data.
Almost all of pedagogical issues mentioned in the relevant literature revolve around
handling those instances of OTC that are described in this analysis as tokens of the Contrastive
variety. For example, DeCock (2000) finds that native speakers of French from the International
Corpus of Learner English (ICLE) overuse OTC in comparison with native speakers of English
(p. 60). That is well enough, but she then goes on to suggest that at least some of this overuse is
due to “misuse”, and favorably quotes Granger and Tyson (1996) in suggesting that this and
presumably other examples of French learners’ overuse and “misuse” of on the contrary is
“probably due to a confusion with the French ‘au contraire,’ which can be used to express both a
concessive and antithetic link… learners seem not to recognize the extra specificity of the
antithetic link” (pp. 22–23). DeCock (2000) offers the following passage from the ICLE corpus
as an example:
(18) For instance, one student coming from a working class family does not have the
same advantages a student whose parents are doctors or lawyers. The latter can
make convenient use of his parents' wealth... The poorer student, on the contrary,
cannot benefit from all these avails. (p. 60, DeCock’s example 1)
25
This example, which DeCock describes as an incorrect use of OTC, is shown from corpus data to
be instead a straightforward and syntactically well-formed example of the Contrastive variety, as
defined and discussed in section 1.1 beginning on page 21. That is, the “extra specificity of the
antithetic link” that DeCock mentions is certainly present in Corrective OTC (the default case),
but contrary to DeCock’s comments, the latter is not the sole definition of OTC that is still viable
in corpus data. Here it should quickly be noted that DeCock’s assertion does not necessarily
invalidate Granger and Tyson’s (1996) findings. However, the latter offers only one example of
such misuse:
(19) This kind of union will be economic. Therefore, I think nobody will have to fear
for his cultural identity. On the contrary if Europe achieves a political union one
day, the European citizen will have to destroy what made him belong to his previous
nation.
Once again, the example they provide contrasts two separate referents: economic unity versus
political union (and by implication in the text, cultural union goes hand in hand with the latter).
The two referents are contrasted along a range of their impact upon the cultural identity of the
people being unified: they are depicted as polar opposites in that one union will leave the cultural
identity of its members intact, while the other union will purportedly destroy that identity.
Semantically at least, this is a straightforward example of Contrastive OTC, albeit made less
palatable by the sentence-initial position of the discourse particle. However, it is marked as an
error.
Similarly, Paquette (2000), as an EFL teacher in Japan, suggests that on the contrary “is
used very often in the papers I proofread, and it is almost always used incorrectly.” Regarding
the output of ESL students in the U.K., Lake (2004) raises similar concerns:
26
a large proportion of [L2] writers who use [on the contrary] appear to do so
inappropriately, often confusing it with on the other hand...Where the student has written
on the contrary, it looks as if on the other hand would be more appropriate. In my
experience as an EAP teacher, this is one of the most consistent misapplications of a
written lexical unit. (p. 137)
Lake’s remarks above clearly describe the semantic role and discourse function of the
Contrastive variety, but he relies on native speaker intuition to characterize it as an inappropriate
use of OTC. The Contrastive variety, however, is still alive in U.S. usage, although it may be
more relevant in the context of British English, as will be seen with reference to the BNC and
BAWE corpora (section 4.5, beginning on page 118). Given that Lake was writing about students
in the U.K., his assertions might need an added layer of explanation.
Instances of Contrastive OTC from both recent and relatively distant past can be found in
the COHA, BAWE and BNC corpora. A striking example of the British English expression of
Contrastive OTC written in the year 1813 (a year in the earliest decade covered by the COHA
corpus) can be seen in this snippet from the final paragraphs of chapter 4 of Jane Austen’s classic
novel Pride and Prejudice (Austen, 1813). The rhetorical goals of this passage are to vividly
underscore the subjective nature of the contrast being drawn, and the sense of complete
opposition within that contrast:
27
(20) The manner in which they spoke of the Meryton assembly was sufficiently
characteristic. Bingley had never met with more pleasant people or prettier girls in
his life; everybody had been most kind and attentive to him; there had been no
formality, no stiffness; he had soon felt acquainted with all the room; and, as to Miss
Bennet, he could not conceive an angel more beautiful. Darcy, on the contrary, had
seen a collection of people in whom there was little beauty and no fashion, for none
of whom he had felt the smallest interest, and from none received either attention or
pleasure. Miss Bennet he acknowledged to be pretty, but she smiled too much.
(ch.4)
It might appear at first glance that there is only one referent (“the Meryton assembly”). However,
further inspection reveals that the contrasted elements are Bingley’s wholly subjective
perceptions versus Darcy’s. The contrast is intended to offer insight into the two characters’
opposite natures. Note that OTC falls subsequent to a noun phrase (the proper noun “Darcy”), as
is characteristic of the variety (see section 4.3 beginning on page 101).
The most recent instance of Contrastive OTC in the COHA database that was verifiably
written by a native speaker of U.S. English (as verified within this present research) dates to
2003 (bear in mind that the corpus data only extends to 2009). The ten most recent examples are
provided in Table 6:
28
Table 6: Ten most recent examples of Contrastive OTC in COHA data
Year* Genre Text English variety
1990 NFThe fact is that the differing reference approach demands that li and ki be discussed apart from one another, while on the contrary the approach that looks to the feelings' source of origination, an actual…
Unknown
1993 NFIn the Mogul empire, on the contrary, the art, at first imported from Iran, took on a vigorous independent form. [Note: Three tokens of Contrastive OTC in text]
Unknown
1995 FIC
Teddy Warner thought history...is best expressed in the terms of a phallic metaphor. I, on the contrary, believe that history is the story of struggle and resistance against and sadly often a submission to domination, oppression and the constant pressure of stupidity, greed and inertia.
USA
1998 NF
… the mirrors remain in thermal equilibrium because Angel's mirrors are "ventilated" with ambient air blown through the hollow honeycombs. On the contrary, thick, zero-expansion glass mirrors retain heat much longer.
USA
1999 FIC Her mother, on the contrary, had given her the impression that Chile was Machu Picchu, and that the Chileans were all Indians. Unknown
2000 NF
The Cardinal's chiastic trope renders paradoxical the principle expressed in Louis's antimetabole (that an object can only yield the quality contaminating it); Pandulph, on the contrary, claims that the last vestige of evil seems most evil but actually is a sign of impending goodness.
USA
2001 NF He on the contrary was determined to speak out and polemicize. UK/British English
2003 NF
Nuts have monounsaturated and polyunsaturated fats, and according to the IFIC, individuals with diets high in these fats enjoy lower levels of bad cholesterol. Saturated fats, on the contrary, increase “bad,” low-density lipoprotein (LDL) cholesterol.
USA
2004 NFAesthetic illusion, on the contrary, has an affinity with emotional involvement, and this in turn correlates with seriousness, as can also be seen in drama.
German
2004 NFHis wife, on the contrary, is reported to have said in a choked voice that she would not hesitate to shoulder the responsibility of her husband and the two female children henceforth.
India/British English
* Note that date from the decade of the 2000s is incomplete in the COHA corpus.
These examples provide counter-evidence to the airtight nature of some EFL instructor’s
intuition-based assertions that use of Contrastive OTC is an “error”, as well as to the
monosemous definitions offered in many reference works. Though relatively rare in U.S. corpus
data, and more common in non-U.S. usage, Contrastive OTC is still a viable form.
As an example of the pedagogical uncertainties presented by relying on native speaker
intuition when explicating Contrastive OTC, consider the following token (21), which was
29
offered as an instance of incorrect use of OTC in a chapter about grammar within an Applied
Linguistics textbook (Larsen-Freeman and DeCarrico, 2010):
(21) There are a lot of mountains in the West; on the contrary, there are few in the
Midwest. (p. 33)
This token was labeled incorrect/unacceptable in the answer section of the textbook. This was
explained as follows:
[Error in Meaning]. The logical connector ‘on the contrary’ usually denies a proposition.
A connector like ‘in contrast’, to compare two things, would be better. (p. 268)
Although the data in this present analysis agrees that in contrast/by contrast has clearly been
preferred in COHA corpus data for U.S. speakers of English over the past two centuries (see
section 4.6, page 122 for a discussion of the historical competition between this term and
Contrastive OTC), the explanation above does not fully or adequately address the actual
semantic and syntactic properties of OTC.
First, the suggestion that the source of the “error” is limited to its “meaning” (here taken
as synonymous with “discourse goal”) is incorrect. The syntactic form chosen here is ill-suited to
the semantic goals of the context. In the example, OTC has been employed as a conjunctive
adverb bracketed by a semicolon and a comma, and thus has been placed between two clauses.
Clause-initial OTC very strongly selects for Corrective OTC; however, in example (21) it has
been employed uncharacteristically in a Contrastive context. Second, there is no obvious
categorical distinction between propositions P1 and P2, or its categorical distinction is less than
absolutely certain and clear. The distinction between a quantity considered “many” and another
considered “few” is imprecise, context-bound and subjective, and does not clearly suggest
30
“complete opposites.” Instead, there is considerable overlap possible between the ranges of
values that might fit either description. Although this property is less indicative of an error than
the previous one, it does add to the overall sense of “unacceptability” of this particular example.
Altering the example to better meet both the syntactic and semantic constraints improves the
acceptability of OTC, as can be seen in example (22):
(22) There are a lot of mountains in the West. In the Midwest, on the contrary, there
are none.
As reformulated above, OTC is positioned after a prepositional phrase and bracketed between
commas, which is a common syntactic form for the Contrastive variety (see section 4.3 on page
101). Moreover, the previous comparison between “a lot” and “a few” has been recast to read “a
lot” versus “none”, which is an unambiguously categorical distinction. In this case, for a given
predicate, there are no contexts within which the two terms (adverb versus pronoun) could
possibly modify or index the same referent. Moreover, “none” is an objective quantification,
since an assertion of the existence of an empty set can easily be verified.
1.2. Alternative OTC and realis versus irrealis domains
Alternative OTC can be considered a subset of Contrastive OTC. It bears a number of
meaningful similarities to both the Corrective and Contrastive varieties, but also bears crucial
distinguishing characteristic when compared to the other two. A few examples of Alternative
OTC drawn from the COHA corpus are offered below (Table 7):
31
Table 7: Examples of Alternative OTC
(23)The upsurge [in heroin overdoses] could have been caused either by toxic
adulterants or, on the contrary, by unusually pure and therefore more potent
supplies of the drug.
(24) I for one am not at all sure that these men of action are doing great good, or any
good at all, or even, on the contrary, that they are not doing great harm.
(25) Chance impulses are like a hoop which outruns the child that has set it rolling; its
very speed condemns it, when left to itself, to meander and to flop. If, on the contrary, the impulse is an adopted one, and needs to be reawakened, it is more
likely to be maintained, for its cause recurs.
(26) According to old folk belief, if the Saint Barbara's grain grows fast, crops will do
well in the coming year. If, on the contrary, it withers and dies, the crops will be
ruined.
As can be seen in examples (23) through (26), each instance has only a single referent. In these
examples, the relative referent is (in order): an upsurge of heroin overdoses; the end result of the
actions of an identified group of men; impulses, and the growth of the crop of St. Barbara’s
grain. However, the references in each example are mutually exclusive, and presented as
constituting polar opposites.
Similar to Corrective OTC, the Alternative variety comprises only a single referent and two
competing and mutually exclusive propositions about that reference. Once again, these
references are portrayed as being qualitative opposites (via the semantic root of the word
“contrary”), and the speaker/writer desires to depict the difference as an emphatically strong and
clear one. However, Alternative OTC does not correct a premise about that referent; rather, it
offers two mutually exclusive potential states or explanations that could be used to describe its
32
sole referent. In short, it contrasts two possible irrealis references for that referent. Alternative
OTC is similar to Contrastive in that its rhetorical goal is contrastive rather than corrective. In
that sense, it could be considered a subset of Contrastive OTC. However, it differs from
Contrastive in four respects: first, it has but one referent; second, it deals with an irrealis domain
while Contrastive OTC is always firmly rooted in (at least, perceived) realis; third, while the two
referents for any instance of Contrastive OTC co-exist within the same time frame and context,
the two references for Alternative OTC are presented as being mutually exclusive. Finally, there
are important differences in syntactic form and current usage rates which make it pedagogically
meaningful to distinguish them as separate categories.
Although Alternative OTC shares many tangible qualities in common with its two
counterparts, perhaps the most noticeable difference is in illocutionary force. Corrective and
Contrastive have the illocutionary force of emphatically asserting a fact. Alternative OTC, on the
other hand, has a less emphatic communicative goal. The Alternative form is characteristically
sentence-medial, and falls subsequent to either the conjunction or or subordinator if. It is a
conditional construction employed when the speaker/writer wishes to express and contrast two
irrealis possibilities. Indeed, as Comrie (2009) points out, there is no intimation of factuality
inherent in any conditional construction, since neither the protasis nor the apodosis is ever
asserted to be true (p. 79). A key difference between Alternative OTC and both Corrective and
Contrastive OTC is that the latter two deal strictly with a referent or referents in the realis
domain: Corrective OTC with a single referent, and Contrastive with two.
Although rarely found in modern usage, the Alternative form is certainly not entirely
defunct, as a quote as recent as 2011 from a New York Times article attests:
33
(27) Whether this is because our imperial hegemony has overwhelmed the possibility
of even rhetorical resistance or because, on the contrary, the empire is not as
mighty as it used to be, is a topic for another day.
Here again the two premises are mutually exclusive assertions about one referent, as in the
Corrective variety, but are presented as members of a contrast set, as in the Contrastive variety.
Given the pedagogical goals of this analysis, it is worthwhile to distinguish this form as a
third category, separate from both the standard Corrective OTC and the viable minority use of
Contrastive. Key differences include: finely-grained distinctions in their rhetorical goals, the
distinction between realis and irrealis domains, key differences in syntactic form, and significant
differences in usage levels observed in the corpus. It might seem that an approach along the lines
of “classify varieties by their rhetorical goals” would indicate that Alternative OTC is a
subvariety of Contrastive, since both set up a non-corrective contrast set in discourse space. That
is a distinction based on the semantic composition of the particles, and indeed in later statistical
analyses, usage rates of Contrastive and Alternative are compiled to derive a more complete
measure of the interaction between semantically contrastive and corrective particles in the
selected time periods. From a pedagogical standpoint, however, Contrastive and Alternative OTC
are syntactically distinct. More importantly, the COHA corpus data contains no significant rates
of use for Alternative OTC in the past century. In classroom presentation, this form could be
described as a separate variety that is rare, awkward and entirely avoidable.
1.3. Metaphoric imagery
As Fischer (1998, 2000) suggests, every discourse particle has a highly schematic,
underspecified, and invariant meaning, which is further enriched in the context of discourse.
Drawing on the paradigm of the “triadic conception of the sign” in Peircean semiotics (Peirce,
34
1932, p. 2.228; this concept is fully explored in section 3 beginning on page 43), discourse
particles can in general be described as a rather complex (but underspecified) radial network of
signs that are in turn composed of constraints and images. The embedded imagery has
metaphoric origins. A key point in this analysis is the assertion that the sharp contrasts typically
inherent in the discourse contexts where OTC is employed find their initial defining constraint in
a single source: the diametric opposition encoded in the word “contrary.” The use of that word in
locative imagery then provides grounds for two separate but related metaphoric images. As
Hansen (1998) explains, this imagery, including but not limited to primary metaphors and other
metaphoric referents, are connected to one another via a network of radial categories that is
generated in a dynamic model:
...particles may indeed have different senses which are not merely a matter of pragmatics,
but that rather than being homonymous or discrete, these various senses are related, either in
a chain-like fashion through family resemblance, or as referents from a prototype. (p. 87)
More specifically, OTC is placed after an explicitly stated proposition P1, and the particle draws
on metaphoric imagery (Lakoff, 1987; Lakoff & Johnson, 1999) to generate a highly specific set
of constraints to create a discourse-relevant topic domain (a mental space, or schema) that is
asserted to be a dichotomy. Thus the particle’s different senses constitute nodes in a network
radiating from a small set of closely-related metaphoric origins (Fischer, 2006b; Serra-Borneto,
1997) which have a single lexical root. Recourse to this systematic cluster of schemata reduces
the number of inferential paths a comprehender must follow to grasp the speaker/writer’s thought
processes on the topic at hand. The constraints arise from a model of communicative tasks that is
a mutually shared element of the communicative competence of both communicators and
comprehenders as they navigate through the communicative process (Serra-Borneto, 1997).
35
The two metaphors draw on the locative imagery of the two (mutually exclusive) sides of a coin,
and the two (coexisting) sides of a wide river that serves as a boundary between two sets.
In a suitable semantic and syntactic environment, OTC can invoke the appropriate
schema by drawing on the metaphoric imagery invoked by a relevant variety. For example,
simply by employing sentence-initial or clause-initial OTC, a schema is licensed that includes an
axis over which the “coin” will be “flipped”, and OTC after a noun phrase or prepositional
phrase invokes a “river” which will divide the two values to be contrasted. This schema then
functions as a contract for delivery of a second premise P2, under a very specific set of
constraints: the act of flipping or dividing is injected into the semantic schema shared by both
speaker/writer and comprehender, and projected into the stream of discourse. Thus the invariant
component or prototype of OTC is that for all three varieties, insertion of the particle into the
stream of discourse generates a mental frame composed of two opposing mental spaces side-by-
side within the mind of the comprehender. The first “bin” or mental space comes ready-
populated with the content of proposition P1. The second “bin”, once created, is ready to be
populated by relevant content comprised of P2 and related information.
Primary metaphors are near-universal concepts (Grady, 1999, 2009; Lakoff & Johnson,
1999) such as UNFEELING IS COLD, MORE IS UP and FUNCTIONAL IS ERECT that are
used as a framework for more culture- or domain-specific metaphoric expressions such as “the
computer network is down” (Grady, 2005, p. 1600). The metaphoric correspondence between a
coin and any binary or dichotomous relationship is straightforward: the relevant primary
metaphor is DICHOTOMIES ARE COINS. This is illustrated graphically in Figure 1, below:
36
Figure 1: Corrective OTC. Diametrically opposed sides of a coin or disc
The opposing sides of a dichotomy involving two propositions map to a metaphor of contrary
sides of a coin. In the propositional dichotomy, the second proposition replaces and corrects the
first. As the two are mutually incompatible, the chain of logic rejects one proposition and leaves
only its counterpart still available for rhetorical use. A side of a debate has been selected. For a
coin, the act of flipping similarly selects only one side.
Along the same lines, the spatial metaphor of a “river” within the Contrastive variety of
OTC is an instance of the primary metaphor RANGES ARE EXTENDED OBJECTS
(Pulaczewska, 1999, p. 229). The contrary sides of a river suggest a clear contrast between
antipodes along some cline, separated by some critical distinguishing characteristics (Figure 2):
37
Figure 2: Contrastive OTC. Distant, opposite points across a river boundary
The differences between the qualities that lie on opposite sides of “the river” are framed by the
presence of OTC into such an overriding contrast (rooted in the word “contrary”) that the river
could be more accurately likened to a geographic boundary between two opposite “countries”
rather than a river running within a single country. This might be analogous, for example, to the
linguistic and cultural differences between France and Germany on opposite sides of a section of
the Rhine River. Note however, that the “coin” and “river” metaphorical schemata, while clearly
sharing similarities, are still distinct metaphors that reflect an ontological divide between the
different semantic frameworks that each of these two basic varieties of OTC creates around
propositional content.
38
2.2. Viewpoint as a mediator of polysemy
The difference between two major varieties is here described as arising chiefly from a
difference in metaphoric origins. A further characteristic lies in the viewpoints described in each
variety. Schwenter (2000) argues persuasively that polysemous uses of the Spanish discourse
marker si (which is unrelated to Spanish sí meaning “yes”) are mediated by viewpoint within
distinct contexts, recasting what appears to be straightforward syntactic negation into a causal
use, as a prelude to negation in monologue form (Schwenter, 2000):
The refutational meaning of the adversative polysemy is paralleled by the high-scalar
meaning of the causal polysemy. This parallelism results from the transfer of dialogical
features from the adversative use, typically found in dialogues, to the causal use, typically
found in monologues. (p. 257)
Following Roulet (1984), Schwenter (2000) considers these contexts to be somewhere on a
continuum that distinguishes between “monologues (one physical speaker) and dialogues (two
physical speakers), as well as monological (one ‘viewpoint’) and dialogical (two ‘viewpoints’)”
Schwenter (2000, p. 260). As Traugott (2010) explains:
Monologic orientation concerns the extent to which speakers share common ground and
build their argument toward the same or similar conclusions (e.g., and, which signals
agreement or addition). Dialogic orientation concerns the extent to which speakers
contest, refute, or build an argument toward alternative or different conclusions (e.g., but,
modal in fact). (p. 15)
Schwenter thus summarizes these distinctions into four different structures combining
speaker/viewpoint (Table 8, reproduced from Schwenter, 2000):
39
Table 8: Structure of discourse by speakers and viewpoints
(p. 260; Schwenter’s Table 1)
For convenience of exposition, the four combinations are labeled as A, B, C and D in Table 8
above. Within this framework, the one-speaker context of Corrective OTC lies within
combination B, a “dialogical monologue.” In fact, reprise assertion (as discussed in section 2.1)
is employed as a rhetorical device to simulate dialogue and thus convey the dialogical nature of
the semantic frame. Two-speaker OTC, obviously enough, is combination D, a “dialogical
dialogue”, since two people are speaking in a context of disagreement. Both Alternative and
Contrastive OTC, however, fall somewhere on a continuum between “dialogic” and “monologic”
in orientation. It is not clear that different viewpoints are in fact being negotiated; rather, a
dialogic context is invoked in order to add a rather exceptional degree of emphasis to the
perceived categorical difference (in context) between two referents. Thus these varieties of OTC
do share membership in combination B, dialogical monologue, with one-party corrective OTC,
but differ in meaningful ways from the latter. In short, the framework described by Table 8 does
not adequately account for the semantic and syntactic differences between these varieties. For
this reason, this analysis adheres to the assertion that the distinctions in the three varieties of
OTC arise from two different metaphoric origins, although the speaker/viewpoint distinction is
one element of their composition.
40
Finally, authors employing Corrective OTC seek to correct or supplant a truth-conditional
assertion. In both Contrastive and Alternative OTC, however, the truth-value of the second
assertion in no way depends upon the truth value of the first. This leads to the further conclusion
that in terms of discourse or sequential relations, the former is a hypotactic particle, while the
later two are paratactic. According to Redeker (1990), “sequential relations can be paratactic,
that is, transitions to the next topic or to the next point, or hypotactic, that is leading into or out
of a commentary, correction, paraphrase, aside, digression or interruption segment” (p. 369). Key
differences between the three varieties are summarized in Table 9, below:
41
Table 9: Comparing Corrective, Contrastive and Alternative OTC
Corrective Alternative Contrastive
Communicative
goal
Oppose an assertion (P1)
by asserting that P1 is not
correct or not true
Offer alternative
options or
propositions
Explain important
differences between the
referents of P2 and P1
Synonyms in fact, however…in contrast,
on the other hand
in contrast,
on the other hand
Referent and
predicate of P1
and P2
Only one referent, identical
in both P1 and P2. Two
different predicates are
mutually exclusive
assertions about the one
referent – one is “true” and
the other is “false”.
Only one referent,
identical in both P1
and P2. Two
different
predicates are
mutually exclusive
possibilities
concerning the
one referent.
Two referents, and the
way they are “opposite”
is described in the two
predicates.
Sequential relation Hypotactic Paratactic Paratactic
Metaphoric
origin
“On the contrary side of the
coin” (mutually incompatible
or mutually exclusive;
cannot coexist)
“On the contrary
side of the coin”
(mutually
incompatible or
mutually exclusive;
cannot coexist)
“On the contrary side of
the river” (both exist at
the same time but are
somehow “opposite”)
Frequency in U.S.
English corpus
data
Overall frequency has
declined, but it is still not
uncommon in academic or
other formal texts.
Steep decline to
near-zero
Overall frequency has
declined in U.S. usage.
Somewhat more
common in the U.K..
42
3. Peircean cognitive-semiotic model
Following Hansen (2006), this analysis decomposes the sense and meaning of OTC within
discourse into a cognitive-semiotic framework. The virtue of a cognitive-semiotic account is that
it includes a theoretically-motivated explanation for a number of phenomena: first, it details the
cognitive processes involved when a hearer/reader resolves the meaning of OTC in context,
generally as outlined in Table 10 on page 51. Second, it also explains two phenomena identified
by this analysis of COHA corpus data: the fact that two main varieties of OTC existed side by
side one another, and that the particle has undergone semantic narrowing.
Recalling Blommaert’s (2005) definition of discourse as comprising “all forms of
meaningful semiotic human activity seen in connection with social, cultural, and historical
patterns” (p. 3), this present analysis of OTC merges the cognitive-semiotic approach to
discourse particles described by Hansen (2006, pp. 37–39) to the account by Schiffrin (2006) that
treats DMS as indexicals.6 Hansen is among those researchers who would consider OTC (and all
comparable rhetorical devices) to be a “discourse particle” rather than a marker with purely
procedural content (Foolen, 2001). This terminological distinction reflects a belief that the term
“discourse marker”, and the accompanying account of their discourse function(s), may be
misleading (Schiffrin, 2006):
… ‘marker’ often implies that a linguistic item is displaying an already existent
meaning; the term ‘particle’ often implies that a meaning not otherwise available
is being added into the discourse. (p. 333)
Schiffrin and Hansen concur that DMs perform a role more complex than passively “indicating”
(Schiffrin, 2006):
6 In the case of OTC, the particle is in part a cataphoric indexical that refers to a sharply constrained semantic scheme.
43
[As a form of deictic, DMs] have a more complex relationship with context than the one
way path implied by either verb (‘display’ or ‘add’)… they select among possible
coordinates and possible ‘centers’ (points of reference) for those coordinates. (p. 333)
That is, rather than constituting simply “markers” which passively highlight pre-existing
relationships between the two propositions P1 and P2, particles such as OTC are cognitive-
semiotic indexicals that indicate a cognitive scheme into which a semantic framework can be fit.
Once the indexical has been positioned in both parties’ shared semantic workspace, additional
details are typically offered in subsequent text to further define the context that gave rise to the
proposition P2.
More specifically, in the Peircean model that Hansen (2006) proposes, discourse particles
are lexical items functioning as linguistic signs that interrelate with an active process of meaning
creation within a comprehender’s mind. Moreover, OTC imposes a rather specialized set of
further semantic requirements upon the schema. On this view, the discourse particle OTC is a
rather complex (but underspecified) radial network of signs composed of images and constraints.
This model is compatible with a polysemous account, and aspects of language change, as Aijmer
(2002) suggests:
... discourse particles are polysemous items whose meanings can be related to each other
in a motivated way, for example as extensions from a prototype. This is compatible with
the diachronic model of grammaticalisation (pragmaticalisation) in which pragmatic
functions are derived from propositional meaning via certain paths and on the basis of
pragmatic principles. This development may be supported by the core meaning of the
particle. (pp. 21–22)
44
The images have metaphoric origins (Lakoff, 1987; Lakoff & Johnson, 1999), and the constraints
arise from a model of communicative tasks that is a mutually shared element of the
communicative competence of both communicators and comprehenders as they navigate through
the communicative process (Serra-Borneto, 1997).
These signs interact first with the context (or “ground”) of principles inherent in the
language system, and second with the specific given propositional framework. They are a
strategically employed schema of claims regarding the ongoing cognitive processes of the
speaker/writer and structures regarding certain claims made in context (Fischer, 1998; Schegloff,
1982). Thus Fischer (2006b) notes that they have more semantic substance than would a
“marker” that is free of conceptual content:
Discourse particles also display a characteristic semantic structure. They are not believed
to mark anything… [instead, they are] lexicalized form-meaning pairs, whose meanings
are under-specified… Their semantic content consists in claims of ongoing mental
processes, specified by reference to aspects of the communicative situation. Common to
all discourse particles are thus not only their under-specified invariant meanings… but
also the nature of their lexical meanings, which all report on the speaker's mental state….
These underspecified signals of mental processes… [and] are contextually specified by
reference to a certain communicative domain… (p. 432)
Thus the understanding of reality that is being communicated is mediated through the interaction
of signs and their high- and low-level contexts to indicate a propositional assertion.
The theoretical framework supporting this analysis of OTC, then, is a Peircean triadic
relationship between what is being represented (the object), the form in which the object is
represented (the representamen) and the way the comprehender is dynamically interpreting that
representation (the interpretant). Hansen (2006) details the cognitive-semiotic processes (both
45
synchronic and diachronic) involved in the creation and use of discourse markers as signs, which
is in turn based on the paradigm of the “triadic conception of the sign” in Peircean semiotics
(Peirce, 1932, 2.228). These signs – including but not limited to and primary metaphors7 and
other metaphoric referents – are connected to one another via a network of radial categories that
is generated within a dynamic model that not only accounts for the synchronic existence of
related meanings for a particular “marker” or “particle”, but also includes room for diachronic
semantic change (Fischer, 2006a, p. 15). Three basic entities are involved in this model, although
the third (the interpretant) is further decomposed into three separate layers, each of which in turn
interacts with a “ground”, as detailed in Hansen (2006):
1) A “representamen” (i.e. an expression or vehicle; often a linguistic sign).
2) An “object” (i.e. the thing represented).
3) An “interpretant” (i.e. a further, equivalent sign, evoked in the mind of the
comprehender by the original sign). There are three levels of interpretant:
a. An “immediate” interpretant, constituted by the range of potential
interpretations of the sign as such.
b. A “dynamic” interpretant, which is the effect actually produced by the sign on
the recipient in a given context. That is, the dynamic interpretant represents
what is actually “decoded” by the comprehender.
c. The “final” interpretant, which is the effect which would be produced by the
sign in question on any recipient whose circumstances were such that he was
able to grasp the full meaning of the sign. This final interpretant may only be
reached through a process of intersubjective negotiation. (p. 38)
In Hansen’s Peircean approach, each individual example of OTC (in context) is a separate
representamen. The representamen, “[conveys] semantic instructions which the [interactant]
7 Primary metaphors are near-universal concepts (Grady 1997, 1999; Lakoff and Johnson, 1999) such as UNFEELING IS COLD, MORE IS UP and FUNCTIONAL IS ERECT that are used as a framework for more culture- or domain-specific metaphoric expressions such as “the computer network is down” (Grady 2005, p.1600).
46
must carry out in order to grasp the meaning of the sign” Hansen (2006, p. 38). In this account,
OTC offers a considerably more rich linguistic contribution than simply encoding procedural
content.
Hansen (2006, p. 34) further asserts that the representamen “does not determine a unique
interpretant which is valid for all contexts – rather, it should be seen as offering a more or less
restricted range of possible interpretations.” The “object” is the whole of the contrastive
relationship between P1 and P2 as it exists in the mind of the speaker or writer who employs
OTC in discourse. The interpretant is a three-layered dynamic sign in the mind of the
comprehender.
According to Hansen, at least the first two layers of interpretant interrelates with a
corresponding layer of “ground”, which in the Peircean paradigm is understood to mean the
relevant frame of reference. Ground 1 is the relevant aspects of the language system as they bear
on OTC, including pragmatic, syntactic and semantic elements. In terms of the cognitive-
semiotic model, ground 1 is comprised of the entirety of the radial network of potential qualities
of OTC and its potential interactions with conceptual and syntactic features, as well as the
relative correspondence of those with a given variety of OTC. Meanwhile, ground 2 within this
model is the intersection between that network and the present case (the present conceptual and
syntactic environment). Thus ground 2 is a subset of ground 1, derived in context, with irrelevant
aspects of ground 1 pruned away.
For our purposes, then, ground 1 includes at least four things: the lexical roots of the
expression “on the contrary”, the schemata encoded within OTC (as well as shared knowledge of
that schema, and expectations arising from that shared knowledge), the syntactic environments in
which OTC can be appropriately placed, and the interaction between syntax and potential
47
schemata. These four interact with the immediate interpretant formed inferentially via abductive
reasoning in the interactant’s mind. Thus the two contexts just previously mentioned correlate
with a first and second layer of “ground”, which interrelate with the first and second interpretants
respectively.
Here it might be helpful to digress a moment to explain the nature of abductive reasoning.
Abductive reasoning, also known as “Inference to the Best Explanation”, is normally thought of
as being one of three major types of inference, the other two being deduction and induction
(Douven, 2011). Hobbs (2004) provides a clear explanation of the similarities and distinctions
among the three:
In deduction, from P and P ⊃ Q, we conclude Q. In induction, from P and Q, or more
likely a number of instances of P and Q together with other considerations, we conclude P
⊃ Q. Abduction is the third possibility. From an observable Q and a general principle P ⊃ Q, we conclude that P must be the underlying reason that Q is true. We assume P because
it explains Q. (p. 727)
Abduction differs from deduction in that the observed fact does not demonstrate that the
explanation that an individual has selected is inescapably true. That is, other possible
explanations exist. A deduced conclusion is logically and inescapably true, given the facts at
hand. An abductively derived conclusion need not be logically and inescapably true; it only
needs to be the most plausible and economical explanation that an individual can derive from
given information at the present time. A second difference between abduction and deduction is
that only the latter is a monotonic process. An inferential operation is monotonic whenever
adding a further observation or a new premise known to be true to the current discourse-relevant
pool of knowledge cannot alter the original, inescapable truth of a deduced conclusion. More
48
formally, if a set of premises logically entails a conclusion, then monotonicity exists whenever
every conceivable superset (any set of premises that includes all of the first set) must also entail
that same conclusion (Koons, 2013). Abduction, however, is non-monotonic, since new
information can invalidate an abductively derived conclusion, if the new data makes some other
conclusion seem more likely.
Induction, meanwhile, is similar to abduction in one key respect: both lead to conclusions
that are not logically and inescapably true, given the facts at hand. However, induction differs
from abduction in that for the former the individual has in his or her store of knowledge a
number of earlier observations that are identical to the one currently being considered, and the
inductive conclusion is based solely on an assumption about the probability that the current
observation is the same as the past ones. Induction appeals solely to statistical probabilities
(Douven, 2011). Abduction differs in that it takes into account the whole sum of both knowledge
and beliefs that the individual has about “the way things work” in the world, and tries to align the
observed fact with some scenario that is deemed “most reasonable” from among the myriad of
possibilities that could conceivably explain the given observation. Thus abductive reasoning is an
inferential process by which individuals seek to select the most reasonable explanation (or
perhaps more accurately, least unreasonable explanation) for an observed fact (Peirce, 1903).
Returning to Hansen’s explication of the Peircean cognitive-semiotic model as it bears on
discourse particles, the next level of ground (ground 2) is the specific context in which an
individual instance of OTC is placed, including the syntactic environment (the particle’s
sentential placement in context – post-NP, post-PP, etc. – as explored in section 4.3 beginning on
page 101), as well as salient characteristics of the objects or propositions being processed within
49
the framework constructed by OTC. This second ground in turn interacts with the dynamic
interpretant.
3.1. Peircean Ground 1 – Discussion and extended examples
The cognitive-semiotic model discussed in the previous section made mention of the
interaction between an interpretant and one or more levels of ground. Ground 1 was defined
above as the relevant aspects of the language system, including pragmatic, syntactic and
semantic elements. Here ground 1 includes at least four things: the lexical roots of the expression
on the contrary, the schemata encoded within OTC (as well as shared knowledge of that schema,
and expectations arising from that shared knowledge), and the syntactic environments in which
OTC can be appropriately placed. The syntactic and semantic constraints and discourse goals
comprise a model of communicative tasks that is a mutually shared component of the
communicative competence of both communicators and comprehenders as they attempt to
navigate inferential paths that are relevant to current discourse (Serra-Borneto, 1997).
As a concrete example, take the following set of properties of OTC (Table 10, below) as
residing within the ground 1 of a comprehender who encounters a token of OTC as given in
example (28), also below:
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Table 10: Initial set of properties of Ground 1 for OTC (all varieties)
OTC has three meanings: Corrective, Contrastive and Alternative.
In every case, OTC is used to suggest that two things are somehow “opposite.”
OTC is typically placed between two propositions P1 and P2. Each proposition has a referent
and a predicate. Proposition P2 is always immediately subsequent to OTC; P1 is very nearly
always immediately prior (especially in modern usage).
The referents of P1 and P2 can be one and the same entity, revolving around conflicting
assertions regarding its nature, or two entities compared along one cline.
The number of entities in the referents of P1 and P2 is a key detail that helps determine the
function of OTC in that context.
The Corrective variety corrects a perceived misstatement or misapprehension.
By extension, Corrective OTC is strongly correlated with a conceptual frame in which two
propositions (that is, two predicates) regarding one entity (that is, one referent) are being
compared.
Again by extension, since the same entity is being referred to in the P1 and P2 that bracket an
instance of OTC, Corrective OTC is strongly correlated with the presence of a pronoun as the
grammatical subject of the proposition P2. This serves as an indexical to the referent of P1, a
noun mentioned explicitly in prior discourse.
Corrective OTC is “garden variety” OTC; it is preferred/recommended by many reference
sources, and the count of tokens of its use outnumbers the other two by a wide margin.
The Corrective variety is strongly correlated with three sentential positions: sentence-initial,
between two halves of a compound sentence, and sentence-medial, post-VP.
Contrastive OTC emphasizes a distinction between two entities as referents of P1 and P2. It can
compare two entities along one qualitative cline, or one entity through time.
By extension, Contrastive OTC is correlated with the use of unique nouns (and not pronouns) as
the referent of both predicates. The contrast is presented as being stark enough to comprise an
“opposite”, at least in context.
Contrastive OTC is rare in US. English but still can occasionally be found in current use. It occurs
more frequently in British English usage.
Contrastive OTC is strongly correlated with sentence-medial, post-NP position. Sentence-medial
post-PP position comprises a strong minority of usage. Sentence-initial Contrastive OTC is
exceedingly rare in U.S. English; less so in British English.
Alternative OTC is certainly not defunct, but it is the least well-represented among the forms.
It compares two possibilities rather than two things that have been asserted or assumed as real.
Alternative OTC is placed subsequent to the coordinating conjunction “or” or the subordinating
conjunction “if.”
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For the sake of discussion, and given these properties that reside within ground 1 of OTC,
imagine a context in which an individual comprehender encounters the following utterance or
text, with no knowledge of prior discourse or context:
(28) In the east, on the contrary, the clouds are white and fluffy.
Initially, the comprehender would probably note sentential placement – post-PP and bracketed by
commas – as the most salient distinguishing characteristic of this particular token of OTC. In this
instance, given that the comprehender is not aware of the relevant P1, ground 2 as a subset of
ground 1 is comprised of the following properties noted in discourse: the post-PP position of
OTC (in what is presumably P2), and the use of a noun (as opposed to a pronoun) as the referent
of P2, and the mention of visual characteristics of that referent. It would be quite reasonable then
for the comprehender to form five initial suppositions: first, this token constitutes P2, since OTC
never occurs within P1. Second, by extension, a proposition P1 was given somewhere in prior
discourse, since OTC can omit P2, but not P1. Third, this is nearly certainly an instance of
Contrastive OTC, given its post-PP sentential position and the use of a definite noun “clouds” as
the referent of P2. Fourth, within the missing proposition P1, clouds (and not some other entity
or entities) that had been vaguely described as “the opposite of in the east”, were also somehow
“the opposite of white and fluffy.” Finally, given this complement of information within the first
and second grounds for this token of OTC, the reader/hearer might infer that the proposition P1
was similar to this: “Dark clouds run along the western rim of the sky.”
After further reflection, perhaps an additional schema would occur to the hearer/reader:
that an absence of clouds might in some sense constitute the opposite of the presence of white
fluffy clouds. In that case, the missing P1 could potentially have referred to a cloudless sky that
was somewhere that is “not the east.” However, “no clouds” is hardly the most obvious opposite
52
to “white fluffy clouds.” The mention of whiteness and puffiness would seem in this potential
explanation to violate or at least strain Grice’s (1975) maxims of Relation and Quantity. The
fluffy and white characteristics of a set of clouds do not seem to draw a clear contrast to the
concept of “no clouds.” Even taking the time to mention those visual characteristics would seem
excessive if the rhetorical goal is to contrast the presence and absence of clouds, rather than the
characteristics of any group of clouds. For these reasons, an abductive inference process almost
certainly would not conclude that this potential instantiation of P1 is the “least unlikely” one
available.
With these things in mind, the cognitive-semiotic work that Corrective OTC (the most
common variety) performs on semantic elements within discourse can be illustrated through an
extended example. To consider this within the cognitive-semiotic framework outlined above, first
consider a passage from a work of fiction (Gailly, 2005) that contains a Corrective token and a
query on that token, since the Corrective variety is the default understanding of OTC. The two
tokens are underlined and bolded, below:
(29) Oh yes, said the other, I have to meet the guy from the embassy, I told you about
him last year when you came to Copenhagen, you can come with us.
When, said Kerguélen, now? Yes, yes, said the other, come get me and we’ll go. It's
just that, said Kerguélen, I hadn't expected this, I'm in uniform. Not a problem, said
the other, on the contrary . What do you mean, on the contrary? Is it a costume
ball? No, no, said the other, you'll see, I'm sure she'll like you. (p. 38; emphasis is
mine)
In this brief passage from a novel that is not in the COHA corpus, two characters, given as
Kerguélen and “the other”, are engaged in a hurried and rather elliptical bit of dialogue over the
telephone. The first ground (i.e., frame of reference) in their discussion is the low-level linguistic
53
invariant semantic content packaged within the discourse particle on the contrary. The second
layer of ground includes both semantic and syntactic elements. Syntactically, the sentential
position of OTC clearly indicates a Corrective interpretation (see section 4.3 beginning on page
101) that constitutes a bare denial of the assertion that immediately preceded it in discourse. The
second layer of ground rests upon a schema in which Kerguélen will join “the other” and at least
one additional companion as they attend a social event. Kerguélen will have no time to change
out of the military uniform he is wearing as they speak. His hesitance and uncertainty come from
his concern that the uniform may be inappropriate attire for a social gathering. The terse reply of
“the other” includes a token of OTC that functions as a linguistic representamen (a linguistic
token perceived as a sign). Within this interaction, OTC functions to close off a wide range of
inferential paths available to Kerguélen (the comprehender) regarding the qualitative
interpretation of the “social event” scenario. When “the other” utters OTC as a response to
Kerguélen’s hesitance, his communicative goal is to soothe Kerguélen’s hesitance by cognitive-
semiotically indicating an object – that is, the scenario in the mind of “the other” – in which the
uniform will do precisely the opposite of hindering social interaction. That is, OTC here
indicates that Kerguélen’s uniform will have a positive impact on his enjoyment of the social
event.
Unfortunately for Kerguélen, however, the invariant semantic content of OTC by itself
merely directs the hearer/reader to construct a set of interpretations that includes all possible-
world scenarios that satisfy the contextually-derived schematic instruction that “the uniform will
be beneficial”, and then lets the hearer/reader choose the best option from among them. In the
cognitive-semiotic model, the range of scenarios is the immediate interpretant, and the scenario
settled upon by the comprehender is the dynamic interpretant. In many cases, selecting such a
54
“best option” based on encyclopedic knowledge of the circumstances surrounding the assertion
P1 results in only one reasonable and unambiguous interpretation. However, that is not always
the outcome. In this case, since “the other” has offered no further elaboration after employing
OTC as a representamen, this unadorned representamen is at first open to an unhelpfully
unrestricted set of interpretations. The fictional comprehender (Kerguélen) is thus left unaided as
he attempts to formulate his dynamic interpretant by envisioning a social setting in which the
uniform could serve as an asset. Initially confused, he signals his confusion by querying the
token of OTC (“What do you mean, on the contrary?”). The best available (or perhaps, least
unlikely) interpretant that Kerguélen is able to abductively infer from the distinctly sketchy
evidence that “the other” has provided is a scenario in which the gathering might be a costume
ball, in which case the uniform could be favorably presented as a costume. Since Kerguélen is
understandably hesitant about settling on this scenario as the correct interpretant, he follows his
initial query with an attempt to resolve his confusion by cooperatively negotiating the meaning
of the representamen. To do so, he offers his dubious, but perhaps least unlikely scenario for
verification (“Is it a costume ball?”). A second hurried response from “the other” explicitly
cancels the assumption regarding a costume ball (“No, no”), then offers yet another elliptical
elaboration, “I'm sure she'll like you.” This final utterance is intended to help the hearer generate
a new inference that replaces the costume ball scenario. Almost certainly, “the other” had
intended OTC to function as a representamen pointing to a scenario in which some female
(previously unmentioned in their discourse) will be pleased or impressed by Kerguélen’s
uniform.
Kerguélen’s initial misinterpretation, which later discourse revealed to be quite wide of
the mark, is not at all contrary to the general principle that interactants have little trouble
55
understanding the meaning of discourse particles within a given context. Although OTC had
never been intended as an index to a costume ball scenario, Kerguélen’s inference was not at all
unwarranted. Here the shortcoming is the sparse discourse context provided by “the other” rather
than any deficiency in Kerguélen’s inferential skills. Recalling that the comprehender’s
inferential process is an abductive one, and that abductive reasoning is non-monotonic, the fact
that “the other” had not mentioned any female companion prior to employing OTC means that
Kerguélen’s cognitive ground for processing “the other’s” representamen was missing a crucial
detail. In fact, since “the other” had mentioned only “the guy from the embassy” before adding
“you can come with us”, it was quite natural for Kerguélen to presume a cooperative principle
within their interaction, and infer via the Gricean maxim of quantity that “us” indexes only “the
other” and “the guy from the embassy.” This leads naturally to an initial surmise that no female
individuals are germane to the inferential process. Thus the cognitive-semiotic ground that
Kerguélen was working from was not merely incomplete; rather, it actually contained evidence
that would tend to point away from any inferential path that leads to a scenario involving
impressing a female. Had the female been mentioned in prior discourse, OTC (perhaps in
combination with a culturally shared form of exaggerated or evocative intonation as an added,
paralinguistic representamen) would have been considerably more effective in signaling the
intended meaning. Although of course the author of this novel designed this miscommunication
and presented it for aesthetic reasons, the manner in which this fictional scenario plays out
presents an instructive example of the cognitive-semiotic processes involved in the use of
discourse particles in general, and in particular of the use of OTC. In particular, here we see that
OTC functions as a cataphoric indexical to a schema selected abductively given the contextual
ground supplied by the speaker.
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The interpretant is the resulting representation within the hearer’s mind, which is realized as
“a new and more developed sign, which itself has the status of an action prescription” (Hansen,
2006, p. 33). However, the (non-monotonic) abductive process of inferring an interpretant can
and sometimes does evolve in the process of interpretation. In practice, the second (dynamic) and
later (final) interpretants may be considerably different from each other. According to Andersen
(1984, p. 38), the dynamic interpretant is decoded content that results from an abductive process,
and thus has the status of a hypothesis or a “most reasonable guess.” This means that “the
dynamic interpretant may be modified or even rejected in the light of subsequent information”
(Hansen, 2006, p. 33). Presumably the interpretant within Kerguélen’s mind (in the example
above) altered in the case above at some point in later discourse. To examine this evolving
process, consider also the following constructed example of a misuse8 of Contrastive OTC as in
example (30):
(30) * Andrea ate a vegetable. Amy, on the contrary, ate broccoli.
This sentence pair initially offers the hearer/reader a dynamic interpretant requiring the reading
that broccoli is not a vegetable. However, since this interpretant does not interact with any
ground that inherits ambiguity in the definitions of “vegetable” and “broccoli” from its context,
and thus operates only within the default context of an inalterable set-to-member relationship
between the two (since broccoli is unquestionably a vegetable), it is difficult to imagine a context
in which a hearer would permit OTC to contrast with the truth-value of the relationship imputed
within P2. It is also difficult to identify any extended (perhaps figurative or humorous) sense or
context in which broccoli is not a “true” or “real” or “valid” vegetable, without reference to any
specific objective rule that could lead to any conclusions that would fulfill the listener’s 8 Throughout this analysis I will use an asterisk to signify a segment that is unacceptable in the given context, and a question mark to signify one that is (at least on some level) questionable or dubious.
57
expectation that the speaker is making this utterance to offer a recoverable meaning. The end
result is that although OTC here offers two potential readings (literal and figurative), neither
reading has viable interpretations to rescue its apparent lack of sense, so it is rejected with little
or no hesitation. The impact that OTC typically has within any given ground is thus rejected, and
the use of OTC is deemed inappropriate within the final interpretant. The sentence is rejected as
nonsense (and various context-bound assumptions about the speaker and/or her motivation for
offering the utterance will certainly arise). Consider a second constructed example (31):
(31) ? Andrea lives in the U.S. Amy, on the contrary, lives in California.
Here any U.S. state can be substituted for “California.” Obviously, each of the 50 states within
the United States of America is legally a subnational administrative and geopolitical division of
the U.S., and as such proposition P2 violates the constitutive boundary conditions laid out by
OTC (see the discussion of boundary conditions for Contrastive OTC, in section 1.1 on page 21).
At least initially then, P2 seems contradictory and meaningless as a dynamic interpretant.
However, a listener can resolve this contradiction and derive meaning from the utterance by
adding a further layer of abstraction: rather than denying that California is legally a U.S. state,
the assertion may be presumed to deny that California is in some unidentified and subjective
sense not a “true” or “real” or “valid” U.S. state. This qualitative assumption then fulfills the
boundary conditions required by OTC, and offers a reading with a final interpretant that is
apparently an attempt at ironic or sarcastic humor.
Unlike the antithetical relationships inherent in conjunctions such as “but” and
“however”, the antithetical contrast drawn by OTC cannot be employed to offset communicative
content that is unstated and wholly implicit within a text or utterance. That is, the use of
Corrective OTC is only licensed under two conditions: first, there must be a P1 that has been
58
stated very clearly and explicitly. Second, at a bare minimum, the truth-conditional content of
that P1 will be rejected. In the passage above, for example, the proposition P1 is the explicit
statement “[That is not] a problem”, uttered by “the other.” Although the prior discourse involved
more than a little inferential work between both interlocutors to arrive at that point, and further
inferential work is still left for Kerguélen as he tries to establish the details that support the bare
proposition P2 (as discussed above), the proposition P1 is still explicitly present in discourse
prior to the token of OTC.
To accomplish its goal of expressing separate but related semantic schemes of semantic
opposition or contrast, the rhetorical wedge of OTC relies not only on its metaphorical and
lexical roots, but each variety also has its own distinct set of strongly preferred (often to the point
of being obligatory) syntactic forms and semantic constraints. The interaction between semantic
import, syntactic form and contextual constraints is so tight as to constitute an element of the
identity of each of the separate meanings of OTC. This section will describe contextual
constraints in greater detail. As mentioned earlier, the syntactic constraints (i.e., sentential
placement) specific to any given example of OTC are of huge importance, both theoretically and
pedagogically. However, since discussion of this constraint requires in-depth examination of
corpus data, the discussion is deferred until section 4.3 on page 101.
Although the goal of OTC is to make boundaries abundantly and emphatically clear in
subjective context, the two propositions in P1 and P2 need not necessarily be diametrically
opposed or even sharply contrasted in any objective or decontextualized sense. It is sufficient
that they completely satisfy a particular set of semantic constraints within the given discourse
context. Specifically, they must be semantically related, categorically distinct (non-overlapping)
complements, and are described by the speaker/write as somehow opposite in the given context.
59
The asserted “oppositeness” quite typically coincides with subjective evaluation, but it may be
defined by the speaker/writer to serve specific discourse needs in context. In either case, the
categorical distinction between P1 and P2 must be germane to the speaker/writer within
discourse, and the speaker/writer must wish to convey that categorical relationship to the
comprehender. This is illustrated in the constructed example of two-speaker Corrective OTC
below (32):
(32) A: “The walls of the government building were ivory white.”
B: “On the contrary, they were a very pale cream color.”
Here propositions P1 and P2 both have one and the same referent, “the walls of the government
building.” Syntactically, OTC is placed sentence-initially, which is the prototypical location for
the Corrective variety of OTC. This particular case could also be reframed as Contrastive in yet
another constructed example, with the crucial caveat that the revised sentence should include two
referents rather than one (33):
(33) The walls of the government building were ivory white. Those of the museum,
on the contrary, were a very pale cream.
The propositions in this Contrastive example bear two separate referents (government building
walls and museum walls) that are compared, and OTC is placed subsequent to a prepositional
phrase rather than between clauses. The syntactic distinction between the two examples is
definitive, as discussed in the section regarding syntax beginning on page 101. Once again,
however, the only semantic requirement is for the color difference to be held significant in
context. In fact, the mildly odd sense of an exaggerated significance between these color
distinctions arises here from the tension between OTC’s inherent requirement for categorical
60
contrasts versus the lack of such an objectively stark contrast in this example. In the majority of
everyday contexts these color differences probably would not be described as strong, but rather
as gradations of difference within a single set of “light, whitish colors”. The mild oddness of this
instance of OTC then offers additional evidence that the non-overlapping quality of the
propositions within OTC is noticed as a semantic constraint by the reader/listener when
processing a text or utterance.
Here “non-overlapping” can be defined as the condition that one cannot be a subset of the
other, and the boundaries between them must be distinct. Further evidence that the referents of
P1 and P2 are constrained to reside within in non-overlapping categories or sets can be seen in
the following constructed examples (34) and (35):
(34) A: John ate five cookies from the plate.
B: ? Jim, on the contrary, ate four cookies from the plate.
C: Jim, on the contrary, ate only four cookies from the plate.
(35) D: John took four steps before diving off the springboard.
E: Jim, in contrast, took three.
F: ? Jim, on the contrary, took three.
Absent any context shared within prior knowledge or discourse between the interlocutors,
utterance B seems an awkward response to A. Perhaps its slightly marked nature might make it
seem a mild attempt at humor. This is true firstly because use of OTC in B seems to clash with
the expectation that P1 and P2 (that is, A and B) will present propositions that are somehow
categorically opposite. Although the numbers four and five are certainly distinct quantities, four
cookies is a subset of five, and even more importantly, the two values are near neighbors in a
numerical range. In any case, it requires some effort to construct a mental model that would
61
comprise or contain some categorical distinction between the two numbers, absent any clarifying
context. However, Example C adds the quantifier “only”, used here as an adverb to emphasize
that the amount is smaller than expected. This rearranges the context to create a categorical
distinction between values within the schema of an expectation, and implies a context in which
the expectations have been communicated and a numerical boundary has been set. Thus C is a
reasonable response, at least within discourse that evaluates the issue of whether either party
violated the expectation of meeting a previously-established boundary condition. If for example
John and Jim had been required to eat a minimum of five cookies, then in example C above, John
complied with the expectation but Jim did not. If instead they were permitted a maximum of
four, then Jim complied, but John did not. In either context, a lower or upper bound has been set,
and a value that violates the boundary condition is not considered a subset of one that does not.
Finally, example E, in a case similar to the one just discussed, shows that the simple contrast
inherent in the meaning of “in contrast” is amenable to use in cases in which P1 and P2 are not
immediately seen as categorically distinct in any objective sense, but as gradations of difference
within a single set. However, absent a compelling schema in which boundaries have been drawn,
the bare context is not sufficient for the stark categorical distinctions inherent in the use of OTC.
Another characteristic of the discourse particle OTC is that it may only be used to contrast
or cancel a proposition that has been explicitly presented in prior discourse. As an example, the
first two of the following utterances, originally from Dascal and Katriel (1977) and repeated by
Bell (1998), were originally famed as an exploration of but, but will serve here to help discuss
this aspect of the pragmatic framework of OTC:
62
(36) A: Shut the door
B: O.K., but don't give me orders.
* C: O.K., but on the contrary, don't give me orders.
D: On the contrary, don't give me orders.
* E: On the contrary, but don't give me orders.
Utterance A communicates both an explicit and implicit messages: first, Speaker A explicitly
communicates a command that the door must be shut. At least in the given context, and perhaps
in all contexts, Speaker A also implicitly asserts an asymmetrical power structure in the
relationship. Given this power structure, A can disregard social niceties and issue unadorned
directives to B.
Here C is not an acceptable response. In its Corrective guise, OTC can only operate when
an explicitly stated premise is cancelled. In the case of C, the initial “O.K.” signals instead
compliance with the command and agrees to perform the requested act. The presence of OTC
should signal that subsequent discourse will correct or contrast with previous discourse, but the
illocutionary force in the previous utterance is only recoverable by inference, and so OTC does
not have the ability to cancel it. This then leaves only one reading available – a self-contradictory
utterance in the speaker first communicates compliance with the overt request via “O.K.”, but
then communicates rejection instead via OTCs. In the absence of any verbal or nonverbal cue
communicating a later rejection of the initial compliance (e.g., “hang on, on second thought”),
the second speaker’s meaning is self-contradictory. Example E is similarly self-contradictory. In
that example, OTC cancels the command to shut the door, and the enriching content rejects the
illocutionary force of A. Those two messages are consistent with one another. However, they are
separated by the conjunction but, which here syntactically indexes a state of disagreement or
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inconsistency. The explicit content of A cannot be negated via OTC without also necessarily
negating the implicit illocutionary force.
On the other hand, examples B and D are both acceptable as a reply to A, but offer
different responses to the two layers of meaning recoverable from A. The initial “OK” in
response B “accepts the propositional content of A’s utterance,” while the subsequent “but don't
give me orders” constitutes a rejection of its illocutionary force (Bell, 1998; Dascal & Katriel,
1977). Response B thus complies with the explicit command on one level but rejects its
underlying message on another, since the conjunction but has the ability to contradict an implicit
illocutionary force. In contrast, the token of Corrective OTC in D cancels the explicit request
(and only the explicit request). Subsequent enriching statements are needed to further cancel the
illocutionary force of that request. The fact that responses B and D are meaningfully different is
also signaled by the fact that B requires the presence of but, which signals that some part of the
initial message of compliance will subsequently be canceled or modified, whereas introduction
of but into an instance involving Corrective OTC disturbs the logic of the utterance and renders it
malformed, as shown in example E. In response C, the initial “OK” again communicates
compliance with the request, but the subsequent OTC cannot operate on the implied illocutionary
force, the utterance is again poorly used and self-contradictory.
The key point to this illustration is that OTC can be used to respond to the explicit
assertion “Shut the door”, but not to the illocutionary force, which is an implicit assertion of an
inherent power structure. That implicit assertion is only subsequently contradicted by the
explicit, enriching content that follows OTC. Thus OTC takes explicitly stated content as its
starting point, which places restrictions on the environments where it can be used, and has a
determining impact on the meaning that is recovered by an interlocutor or reader.
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3.2. Polysemy and language change
Finally, Hansen’s Peircean cognitive-semiotic model has the additional advantage of
offering a motivated account for polysemy among discourse particles, and implications for
diachronic change. As Hansen (2006) explains at length:
…should a sufficiently large number of comprehenders produce more or less similar
chains of inference when interpreting the situated uses of a given linguistic sign in a
sufficiently large number of contexts, the speaking community may well end up by
establishing a new interpretive habit which will henceforth form a part of the meaning of
the sign “as such.” In other words, thanks to the frequency of a particular kind of
dynamic interpretants, level 1 of the sign in question may be abductively modified,
resulting in either polysemy or actual semantic shift. (p. 39)
Given the definition of abduction as explained above, Hansen thus holds that, within a given
discourse community or community of practice, a shared history of identical, abductively derived
conclusions about the form and function of a discourse particle can coalesce via social processes
into a critical mass of occurrences. Speakers/writers refer either implicitly or explicitly to that
critical mass, in turn, as credible evidence (or social proof, see discussion of Cialdini, 2001,
below) against a less-favored or less-frequent form of usage. Over time, this can result in
reduction and eventual outright elimination of one variety of a polysemous form, at least among
one discourse community or community of practice. In the data at hand, this seems to have been
the case for the Alternative variety of OTC, across all genres studied. The reverse is quite
naturally also possible: polysemy can arise in the same manner as a “new interpretive habit” and
involve the addition of a new meaning to existing ones.
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This process is completely in accordance with principles of cognitive grammar. As
discussed in Langacker (2013, p. 47–49), the meanings of lexical items are never fixed, instead
residing within a complex conceptual matrix of overlapping constitutive domains, and those
domains are preferentially ranked in terms of accessibility or centrality within a given context,
making one more likely to be accessed than another within a given context:
Ranking for centrality implies that a lexical meaning, even if open-ended, is not totally
free or unconstrained. A lexical item is partly defined by the likelihood (sometimes
approaching categorical status) of particular domains being activated. It thus incorporates
conventional ways of accessing a certain range of encyclopedic knowledge. At the same
time, a lexical meaning is never totally fixed or invariable, for several reasons. First, the
inclination for a given domain to be activated is probabilistic rather than absolute.
[Second] is that the probabilities are subject to contextual modification. Finally they vary
through time based on the vicissitudes of usage. (p. 48–49)
In the specific context of OTC, then, the “either/or” dichotomy of the Corrective interpretation is
preferentially ranked, nearly to the point of the categorical status of a solitary definition. The
less-conventional Contrastive interpretation, however, intrudes upon the over-simplified “one
meaning” account when two elements within a contrastive cognitive domain are considered so
starkly different that an either/or schema can be evoked, and when evoking such a schema
coincides with the speaker/writer’s rhetorical goals. Finally, this process of selecting a central
element within a locus of meanings and among overlapping domains not only occurs
synchronically within the deliberations of a single speaker/writer, but diachronically among
members of a speech community.
The interaction between the probabilistic inclination for any language domain to activate
a vocabulary item and the “vicissitudes of usage” that Langacker invokes can be better
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understood as the interaction between language-internal and language-external factors. Language
change that adds new meaning to a word or phrase is thus a process in which internal and
external factors work together when speakers use language in a novel manner in order to co-opt
linguistic features from one domain to another, often by inviting inferencing on the part of the
listener, as Traugott (2010) notes below. The quote is offered at length, to clearly show its precise
and consistent correspondence to Hansen’s (2006) Peircean model:
… invited inferencing as a motivation for change combines both external and internal
perspectives. It is assumed that speakers act (invite addressees to interpret) exploiting
language-internal implicatures (Traugott and König 1991, Traugott and Dasher 2002).
After becoming salient in a community (a social factor) such implicatures may become
conventionalized (coded or semanticized) via semantic reanalysis (an internal
mechanism). For example, speaker-based, subjective meanings may become salient in
certain types of communication as a result of certain interactional practices, but the
process of “subjectification” is the reanalysis or semanticization of speaker-based
meanings, such as are expressed by epistemic modality or discourse markers. It is an
internal mechanism that operates on outcomes of externally motivated interaction. (p. 13)
In the same way, language change that involves semantic narrowing can occur when one
particular usage becomes so valued or preferred within a greater discourse community or
community of practice that alternate forms are devalued. Across a number of points, this
explanation precisely echoes the one in Hansen’s Peircean model. Moreover, within a lexical
economy as posited by Lüdtke (1985, pp. 359ff), semantic change within one form can also have
an impact on other words and phrases within a given semantic field. New meanings may be
added to other existing forms to make up for the reduction in lexical inventory that semantic
narrowing has created. Thus an instance of semantic narrowing in one item triggers semantic
broadening within a separate lexical item to recoup the resulting loss to the lexicon. In contrast,
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semantic broadening in one item within a discourse community’s lexicon may move one
definition of that word into a new semantic field, triggering a process of semantic narrowing in
some other item within that new field, especially if the latter is somehow ambiguous. Thus
interactional processes within a discourse community can exert a social force designed to
maximize a given word’s clarity or perceived appropriateness. This process is explored in further
theoretical detail in section 4.6 beginning on page 122, and is taken as a crucial underlying
assumption within the use of FEVDs in section 4.7 beginning on page 128.
3.3. Discourse communities, genres, chronotopes, and cultural attractors
According to Swales (1990), the concept of genre is very much tied up with the idea of a
discourse community. The concepts of genre and especially discourse community have been
discussed and refined in various ways, the latter in particular as a community of practice (Lave
and Wenger, 1991; see Scollon 2002, pp. 145–147 for a discussion of the development of related
terminology). For the purposes of this analysis, a loose definition is acceptable: a discourse
community is made up of the authors, readers, editors and publishers of a particular genre. Its
members also engage in a number of important dynamic social processes as the community
creates and regulates its own ideology, norms of social practice, and relevant linguistic output. A
genre is all the body of recorded language (usually written, but could be videos, movies, etc.)
that a given discourse community creates by itself and (mostly) for itself. Thus for example
romance novel authors, readers, editors and publishers are linguistic agents that comprise a
specific discourse community, and romance novels are the genre that that particular community
creates. The genre of romance novels can be divided into sub-genres: historical romance, modern
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romance, and so on. And the genre of romance novels is itself a sub-genre of the larger Fiction
genre.
Discourse communities or communities of practice dynamically self-select emergent
ideological constructions via social processes, which Sperber (1996) has referred to as cultural
attractors, that create a tendency through time for ideas to coalesce around a locus of meaning.
Within the field of literary analysis Bakhtin (1981, p. 250) refers to these loci as emergent
chronotopes, within which multiple agents of a discourse community negotiate the discursive
production of social identities and norms of practice, using different domains of knowledge and
genres of discourse as their media. Such communities are neither monolithic nor stable – they are
instead “heteroglossic, overlapping, permeable, dynamic, generative ideological constructions”
(Kamberelis & Dimitriadis, 2004) that function to organize discourse around a set of central
ideologies or “parameters of value” that they themselves have defined (Morson & Emerson,
1990, p. 369). The meaning of a given word is among those things that can be altered within the
“norms of practice” of Bakhtin’s chronotope; individual members of a discourse community are
specific examples of both comprehenders and communicators in the Peircean model.
The parameters of value inherent in a chronotope are analogous to Sperber’s cultural
attractors. These can probably be defined still even more precisely by appealing to the dynamics
of social psychology, with reference to Cialdini’s (2001) “principles of attraction.” Cialdini’s aim
was to describe micro-level persuasive devices or compliance techniques, but these can almost
certainly be extended to macro-level phenomena that can operate within a self-reinforcing social
dynamic of cultural change (and more specifically, language change). Cialdini (2001) described
six such dynamics: authority, reciprocation, commitment and consistency, social proof, liking,
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and scarcity. Four of these have particular relevance to language change: authority, commitment
and consistency, social proof, and liking.
The first applicable principle in Cialdini (2001), authority, suggests that decision-making
processes are influenced by the presence of a “voice of authority” that lends guidance in an
uncertain case. This has obvious applications: a respected reference work can influence the
editors of various publications, who in turn influence authors and readers in a “trickle down”
effect. Although linguistic norms are always open to change and challenge (Cameron, 1997), the
presence of influence of authority upon social dynamics has support in literature from many
fields.
Consistency of linguistic usage is an explicit and overriding goal of prescriptive
approaches to language. Once again, Cialdini’s model refers more to commitment and
consistency as a psychological dynamic at an individual level rather than as an explicit social
goal: “[o]nce we make a choice or take a stand, we will encounter personal and interpersonal
pressures to behave consistently with that commitment” (Cialdini, 2001, p. 53). However, the
internal, personal pressures that Cialdini notes certainly serve to reinforce whatever external
social pressures may arise from appeals to prescriptive authorities. These pressures again
function as binding or glue on a growing dynamic of cultural change; they are among Sperber’s
“cultural attractors.”
The third of Cialdini’s principles which seem applicable, social proof, has already been
mentioned above. As anecdotal experience from my life as an expatriate in Taiwan, I have been
told that in Taiwan people who are searching for a restaurant will shun establishments that seem
empty and consciously choose those that have more customers and are thus more rè nào (熱鬧;
“lively; bustling with noise and enthusiasm”)9. This is partially because the quality of being
9 MDBG Chinese-English Dictionary. Retrieved from: http://www.mdbg.net/chindict/chindict.php?
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rè nào is seen by many as creating an intrinsically appealing atmosphere, but partially also on the
logic that “if many people are eating there, the food must be good.” More formally, this principle
reflects individuals’ appeal to social validation in their decision-making processes, as they
“determine what is correct by finding out what other people think is correct” (Cialdini, 2001, p.
110).
The fourth and final relevant aspect of Cialdini’s (2001) model is the principle of liking.
It should be noted that Cialdini’s model is one of social psychology; it makes no reference to
cognitive effects that hearers/readers (who later function as speakers/writers) may receive from a
word or expression that is deemed somehow particularly elegant, appealing or apt. Thus Cialdini
refers to liking the source (whether an individual, a publication, etc.) of a social phenomenon
such as a lexeme or lexical structure, rather than liking the structure itself. The appealing nature
of individual linguistic phenomena may also serve as a catalyst for language change within an
individual or group, or a group influenced by individuals.
Langacker’s assertion that lexical meanings “vary through time based on the vicissitudes
of usage” can be further elaborated with respect to the obvious parallels that Hansen’s model has
with an epidemiological approach to the transmission of culture (Atran, 1990, 2002; Bloch and
Sperber, 2002; Boyer, 1994, 2001; Sperber, 1985, 1994, 1996, 1999, 2006), and in particular
with Sperber’s (1985, 1994) “epidemiology of representations” in which the reiterated
communication of representations passes along cultural traits, based on the “catchiness” of a
given trait, and mediated by a process of construction of meaning rather than relying on
replication. It may seem odd to draw a link between Sperber and semiology, since Sperber
explicitly argued against a semiological view of symbolism in Rethinking Symbolism (1975).
However, the dynamic nature of the relationship between interpretant and representamen, in
page=worddict&wdqchi=%E7%86%B1%E9%AC%A7&wdrst=1&wdqchim=3
71
which a representamen has not one possible interpretant but many, and the hearer’s process of
selecting one preferred account among them is developed within an abductive inferential
process, marks this as essentially a cognitive model with semiological elements. This observation
is in line with Sperber’s (2010) informal definition of cognitive processes:
What makes a cognitive process cognitive is that it has as its function to secure a content
relationship between its input and its output... In the case of inferential processes, the
input and the output are both mental representations and the inferential process aims at
securing a relationship of justification: the content of the input, or premises, should
justify that of the output, or conclusion. (p. 68)
The respective models of Hansen (pace Peirce), Bakhtin, Sperber and Cialdini share two
fundamental characteristics: first, that cognition is in many cases a distributed cultural
phenomenon, and second, that a distributed network of social forces (which may or may not
coincide with purely cognitive principles) works through time to shape and transmit cultural
tokens. In the case of language change, the cultural tokens at hand can be any linguistic
phenomenon, including rhetorical devices, lexemes and their meanings. Cultural tokens are
repeated again and again within the discourse community, indexing not only the ideology of the
speaker/writer but other ideologies as well, but carrying within them the forces that work toward
creating a pattern or central tendency of cultural practice. The form and meaning of such tokens
is not perfectly preserved, but does gravitate toward a negotiated center. Each model has a
different focus – Hansen on the interaction between the semiological and cognitive aspects,
Bakhtin on the practical effects of shared and negotiated worldviews or ideologies has on literary
output (and vice versa), and Sperber on the dynamics of transmission within a distributed
cognitive framework – but each of these three foci are integral components of a unified model of
language change.
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The diachronic outcome presented in Hansen’s account matches well with corpus data
examined in this analysis. Based on two centuries of corpus data from U.S. English publications,
OTC is historically polysemous in U.S. usage, but has undergone semantic narrowing (see
section 4.6, page 122). Meanwhile, the Contrastive usage seems to have remained more viable in
U.K. written discourse. At least in U.S. usage, as social forces working in favor of the creation of
a cultural norm, such as Bakhtin’s (1981) “chronotopes” or Sperber’s (2006) “cultural
attractors”, come into conflict with one another around the area of definition and use of a given
lexeme, the data across two centuries of usage displays a dynamic of semantic change. One
metaphor-based usage has prevailed over another and the number of tokens of the second variety
dwindles nearly into nonexistence. Results from a related analysis reveal that all three varieties
of OTC (Corrective, Contrastive and Alternative) display a clear trend of declining usage over
the time frame covered by COHA (see Figure 3 on page 85).
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4. Diachronic analyses of corpus data
4.1. Data and Methodology
The theoretical account in this analysis is supported by an extensive corpus-based analysis,
noting both broad trends and specific statistical findings regarding tokens of OTC (sometimes in
conjunction with other discourse particles) in US usage across nearly two hundred years of
COHA corpus data within four genres (Fiction, Nonfiction, News and Magazines). A total of just
over 8,128 tokens10 of on the contrary (OTC) obtained from the COHA corpus were examined
individually, each within its larger context, to determine which of the three varieties of OTC it
should be classified as (Corrective, Contrastive and Alternative; see Table 29, page 181 for
summary data). The COHA corpus is composed of more than 400 million words drawn from
more than 100,000 individual texts. The major sources drawn on by the COHA corpus11 for each
genre are as follows (Table 11):
Table 11: Composition of COHA Corpus
FictionProject Gutenberg (1810-1930), Making of America (1810-1900), scanned
books (1930-1990), movie and play scripts, COCA (1990-2010)
Magazine
Making of America (1810-1900), scanned and PDF (1900-1990), COCA
(1990-2010). In each decade, the magazines are balanced across at least ten
magazines (with equivalent sub-genres for the 1900s)
Newspaper PDF > TXT of at least five newspapers (1850-1980), COCA etc (1990-2010)
Non-fiction Project Gutenberg (1810-1900), www.archive.org (1810-1900), scanned
books (1900-1990), COCA (1990-2010). In each decade, the non-fiction is
balanced across the Library of Congress classification system
Decennial word count totals by genre, for each variety, for the COHA corpus is presented in
Table 28 on page 180. Using text titles, author’s names, and publication dates provided within
10 A number of tokens were deleted from the count for reasons such as: original text published before 1800, token of OTC found within a quote that predates the COHA date range, etc. See Appendix B: Texts from COHA corpus deleted from this analysis on page 191.11 Information regarding the composition of the corpus may be found at: http://corpus.byu.edu/coha/help/texts_e.asp
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the COHA corpus, in conjunction with extensive research of individual authors’ biographical
data, data for the Fiction and Nonfiction genres has also been decomposed into native and non-
native speaker of U.S. English for examination of trends through time. As supporting evidence,
synchronic data from the BNC and BAWE corpora are also referenced for comparison.
Trends in the data were investigated using figures per 10,000 words of text within the
corpus data for each genre, using first decennial then yearly data. Queries were performed on the
Brigham Young University’s Corpus of Historical American English (COHA) corpus to obtain
diachronic data for the discourse particles however, on the contrary, in contrast and by contrast.
Although the COHA data includes the time frames from 1810 through 2000s, closer inspection
suggests that the COHA data for the first decade is too sparse for robust results (and often simply
non-existent), so the earliest date range the analyses encompass begins in the decade of the
1820s. For the OTC results, great effort was expended to screen for the presence of duplicate
entries or reprinted texts that were originally published prior to the specified date range (see
Appendix B: Texts from COHA corpus deleted from this analysis, page 191). Word count totals
for corpus texts were computed by summing the word counts of each individual text, rather than
relying on the summary table found on an Excel spreadsheet on the COHA website.12
This data was examined in a number of ways. Initially, decennial data was converted into
graphic form via charts generated by Microsoft Excel. These are discussed in terms of the “big
picture” of trends through time for each discourse particle, particularly in sections 4.2, 4.3, 4.4,
and 4.6.
Given the nature of the linguistic domain at hand, statistical methods such as Ordinary
Least Squares (OLS) may not be appropriate for the task of investigating the polysemy of OTC.
A new approach is thus desirable: forecast error variance decompositions (FEVD, also known as
12 http://corpus.byu.edu/coha/files/cohaTexts.xls
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innovation accounting) derived as an ancillary to vector autoregression (VAR) modeling. One
key contribution of this present analysis is the assertion that FEVDs as an ancillary of vector
autoregression analysis can be imported fruitfully into Applied Linguistics research that utilizes
corpus data. In particular, FEVDs can be used to generate a new approach to creating Behavioral
Profiles (Gries, 2010b; Hanks, 1996) that is new to lexical semantics, based on vectors of
Granger-causality between lexical items (here, discourse particles). Other concepts and methods
of VAR analysis, such as impulse response functions (IRFs) and historical decomposition, may
be employed in future research. In section 4.7 vector autoregression analyses are carried out to
generate ancillary forecast error variance decompositions (FEVDs). The latter are used to explore
the dynamics of relationships between the various discourse particles, with the larger goal of
providing supporting evidence for the assertion of polysemy in OTC. Here the word “dynamics”
is intended to indicate the impact that all of the variables in a system have upon each other, rather
than the impact that one or more independent variable(s) have upon one dependent variable.
Vector autoregression analyses are particularly well-suited for such an approach, as will be
explained below. VAR analyses were conducted on COHA data using the free, open source gretl
statistical software package (Cottrell & Lucchetti, 2014).13 In order to construct a VAR model,
tokens of the discourse particles in contrast, however, in fact and on the other hand (OTOH)
were gathered from the COHA corpus and included in the analysis as endogenous variables.
Each of these particles could function as a replacement for OTC in various contexts, with in
contrast and OTOH clearly more suitable as a substitute for Contrastive OTC (Borkin, 1979, pp.
45–46; Fraser, 1998, p. 94), while however could be used in place of either the Contrastive or
Corrective variety (to some extent depending on sentential placement, as a post-NP or post-PP
13 The gretl software package can be downloaded from http://gretl.sourceforge.net/
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position marking focus interacts with the selection of a contrastive information structure, see
Molnár, 2002). In fact was also selected as a complement to Corrective OTC.
Drawing on corpus data, three criteria are thus used to support the assertion of polysemy
for OTC: diachronic usage rates, distinctive syntactic structures and systematic findings within
forecast error variance decompositions. The goal is not to generate a set of diagnostic tests used
to identify a word as being polysemous. Such a goal is far beyond the scope of this research and
would be a dubious undertaking in any event, as Fillmore and Atkins (2000, p. 101) hold that
“even for lexicographers there are no objective criteria for the analysis of a word into senses.”
Rather, the goal is to accept a rough and commonsense concept of polysemy as being sufficient
for the present case, and then examine corpus data in search of patterns that may strongly
correspond to such a definition. To the same degree that these correspondences prove to be
robust, they are then taken as supportive evidence.
This analysis draws on forecast error variance decompositions, generated via VAR
analysis, as statistical means to study the dynamics of structural shocks across different time
horizons. At this point a brief explanation of vector autoregression analysis and relevant
terminology are in order. A VAR approach was adopted here for a number of reasons: first and
foremost, this analysis is firmly situated within a theoretical framework which explicitly assumes
that the dynamics of the relationships between these variables are complex, vary through time,
resist cause-and-effect specification, and are too subtle and flexible to be captured within the
framework of structural models such as ordinary least squares regression (OLS). VARs present
an alternative approach, as Stock and Watson (2001) note:
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Because VARs involve current and lagged values of multiple time series, they capture
comovements that cannot be detected in univariate or bivariate models. Standard VAR
summary statistics like Granger-causality tests, impulse response functions and variance
decompositions are well-accepted and widely used methods for portraying these
comovements. These summary statistics are useful because they provide targets for
theoretical… models. (p. 110)
In contrast, approaches such as OLS by their nature place restrictive assumptions on the data in
order to specify a model. In particular, they require the selection of one or more exogenous
(independent) variables, and often require that perfectly plausible explanatory variables be
excluded in implausible ways (Hoover, 2006, p. 243). Assuming that a researcher adopts such an
approach, how would it be modeled for OTC? Broadly speaking, the paradigm at hand involves
semantic narrowing of an ambiguous (polysemous) information structure (due to canonical
versus non-canonical focus position of Corrective versus Contrastive OTC; see Kruijff, 2002,
especially the Informativity Hypothesis II on pp. 141ff.) and a corresponding semantic extension
of an item in the same semantic subsystem or field. However, deciding whether in/by contrast
(perhaps in conjunction with other particles such as OTOH and however) should be selected as
the independent variable(s) and Contrastive OTC modeled as the dependent variable is a highly
problematic task, to say the least. Intuitively, did authors independently begin to express their
preference for the use of in/by contrast and perhaps other particles, resulting in a reduction or
“crowding out” of Contrastive OTC? Or did they instead refrain from using Contrastive OTC to
relieve the ambiguity (and reduce the cognitive load) presented by its polysemy, and then began
searching for a suitable synonym? Whether this was done specifically to ameliorate the
ambiguity in Contrastive OTC, or merely as a byproduct of other social factors (e.g., prestige), is
an open question (see related discussion at Fortson, 2003, pp. 662–663 n.13).
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At least initially, it would not be unreasonable to approach this question by appeal to an
analogous linguistic process: lexical borrowing, or loanwords. This is precisely the case that
Ullmann (1972) considers: although loanwords in English sometimes take on a broader meaning
than their original non-English sense, it is relatively more common for the native English word to
either be lost, or to undergo changes in register or context that typically involve semantic
narrowing and specialization (Ullmann, 1972, p. 229; see also Durkin, 2014, pp. 406–409;
examples on p. 215). One commonly-cited example is deer (originally deor) which originally
meant “any wild animal.” But when this meaning was supplanted by the French loanword beast,
the native English word deor took on the more restricted meaning familiar today. This example
of semantic narrowing as a consequence of the growing use of a loanword is one of two taken up
in Sagi, Kaufmann and Clark (2011), which employs a Latent Semantic Analysis approach to
analyze corpus data using the public-domain Infomap software. Their findings suggest that the
word deer (or deor) underwent semantic narrowing in a progressive series of stages, and in a
manner that suggests that the changes were far more complex than straightforward cause-and-
effect narrowing. This both supplements and in some sense diverges from accounts based on
native speaker intuition, and highlights a need for further empirical research. The precise answer
to this question may only be resolved by unusually detailed historical sources; the distinction
between correlation and causation is not resolvable by statistical means. However, such precise
historic data is not currently available and may never be available. Indeed, even if such
information existed, the actual dynamics of the change probably involve a complex interplay of
semantic domains, as the results of Sagi, Kaufmann and Clark (2011) suggest.
In contrast to structural methods, vector autoregression systems are designed to assume
that all variables in the model (in this present case, incidence per 10,000 words of a set of
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discourse particles) are endogenous, removing the need to label one or more variables as
exogenous, independent regressors. Since VAR models such as these are constructed with the
assumption that all variables are endogenous, any influence on the interaction between the
variables in the model can only come from its own current and lagged values and the current and
lagged values of the other variables in the model. That is not quite the same as saying that
absolutely no other influences on these variables exist; instead it is saying that the combined
influence of all other possible variables is assumed to be negligible. VAR systems thus “pull as
much information as [possible] from the data with as few structural restrictions… as possible”
(Colander, 2006, p. 65). Rather than placing or even implying any restrictive cause-and-effect
assumptions upon the model and determining the statistical significance of the coefficients of the
independent variables, VAR systems “provide a probability model of the dynamics and
correlations among the data” (Sims, 1980, as summarized in Brandt & Williams, 2007, p. 12).
The goal is to “…trace out the true dynamics of the endogenous variables in response to
structural shocks” (Canova, 2006, p. 2). VAR models thus capture and analyze the rich joint
dynamics of a selected set of variables, and draw conclusions regarding their relationships,
particularly over a specifically-selected time range. As statistical tools, VAR models constitute a
superior approach to considering relationships characterized by “uncertainty and dynamics”, and
changing through time (Brandt & Williams, 2007, p. 14),
Using only a small set of endogenous variables is obviously an abstraction which omits
an indefinable number of individually-created rhetorical options. At the time of composing texts
such as those found within the COHA corpus, any author who wishes to capture and present the
semantic content that OTC contains can select from a very large range of either relatively
standard or more creative stylistic options. For any number of stylistic reasons, the selection
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process is highly subjective. An author may not wish to include any discourse particle in a given
passage, although adding a different discourse particle (such as on the other hand) to the text is
the most obvious choice. All of these options make measuring the significance of any individual
particle difficult to gauge through time. However, the influence of any other variable – in this
case, individual rhetorical device other than these particles – may safely be assumed to be
negligible, which fits well with the vector autoregression approach, since as Hoover et. al. (2008)
explains, among the defining features of VAR models are the facts that in such models:
…theory has implications interpretable in its terms, and… data are described fully
enough that its only residuals are identically independent random errors – that is,
unsystematic noise. The payoff is that such a statistical model warrants the use of
likelihood methods and provides a firm basis for deductions about the implications for
theory. (p. 3)
Impulse response functions (IRFs) and forecast error variance decompositions (FEVDs)
are statistical measures ancillary to the VAR estimation process. They have become important
tools for estimating the impact of a change or shock (also referred to as an “innovation”) in one
variable in a system upon other variables. Within the ancillaries of the VAR process, IRFs and
FEVDs are designed to “show the effects of shocks on the adjustment path of the variables”
(Hill et al. 2011, p. 505). These shocks are not exogenous; they are changes in the errors of other
variables in the VAR system, and so the results represent an “…implied thought experiment of
changing one error while holding the others constant” (Stock & Watson, 2001). One such process
is forecast error variance decomposition (FEVD), also known as “innovation accounting”.
Innovation accounting is used extensively in this analysis. Stock and Watson (2001) describe the
useful explanatory characteristics of this statistical measure:
81
… the percentage of the variance of the error made in forecasting a variable (say,
inflation) due to a specific shock (say, the error term in the unemployment equation) at a
given horizon (like two years). Thus, the forecast error decomposition is like a partial R2
for the forecast error, by forecast horizon. (p. 106)
Intuitively, FEVDs trace out the aggregate impact of all other variables in the system upon the
forecasted error of one variable across a number of time horizons, thus indicating the degree of
overall interaction among the variables (p. 106).
Taken together, these facts suggest that the use of FEVDs, which are well-established in
the field of macroeconomics, offers a new analytical tool to the examination of the polysemy of
OTC. FEVDs can be applied to answer questions relevant to the analysis of a discourse particle
within a period of semantic change. As Summers (2000) notes, their results are a means of
approaching questions such as:
… “if the first variable in the VAR is unexpectedly high [or unexpectedly low] in period
t, how will this affect the other variables between periods t and t+k?” Or, “how important
have prediction errors in variable 3 been in explaining the variance in the forecast errors
of variable 2?” (p. 5)
Within a lexical economy as posited by Lüdtke (1985, pp. 359ff), semantic change within one
form has an impact on other words and phrases within a given semantic field. New meanings
may be added to other existing forms to make up for the reduction in lexical inventory that
semantic narrowing has created. Thus an instance of semantic narrowing in one item triggers
semantic broadening within a separate lexical item to recoup the resulting loss to the lexicon. In
contrast, semantic broadening in one item within a discourse community’s lexicon may move a
given definition of that word into a new semantic field, triggering a process of semantic
narrowing in some other item within that new field, especially if the latter is somehow
82
ambiguous. Thus interactional processes within a discourse community can exert a social force
designed to maximize a given word’s clarity or perceived appropriateness. This process,
embedded within the social fabric of a discourse community and exogenous to the writing
process itself, results in “unexpectedly low” values for a word undergoing semantic narrowing.
This social process cannot be directly observed or measured by examining corpus data. However,
the degree to which a change is expected or unexpected can be probed via forecasting
techniques, and the resulting degree to which such an unexpected change impacts related
variables within the writing process (here taken to be related discourse particles) can be observed
via FEVDs. Thus in a period of semantic change, FEVDs can offer an indirect but nonetheless
powerful approach to teasing apart the nature of the variables undergoing pressure from social
forces. As noted more than once, including in the discussion surrounding Figure 23: Contrastive
OTC, clause-initial In/by contrast 1830s–2000s on page 126, the period of the 1860s through the
1890s offers a potentially fruitful time range for just such an analysis. Examination of the 10-
step-ahead FEVD data, at which time the model is assumed to have taken into account all the
results of the unexpected exogenous shocks caused by social pressures within the discourse
community, can offer crucial clues about the relationships between the observed variables.
One note about the data used in the vector autoregression analyses: despite being
essentially interchangeable with in contrast, the particle by contrast was excluded for the simple
reason that the COHA corpus data did not contain any observations at all for a large number of
periods in the date ranges studied. Yearly data for by contrast in each of the four genres thus
could not present a reliable account of the dynamics between it and other discourse particles.14
For precisely the same reason, analyses were conducted on only three of the four genres that the
14 Note that Figure 21 on page 117 shows a measurable number of instances of the particle by contrast across relevant time ranges. However, that graph depicts data that has been compiled into decennial totals, and is furthermore an aggregation of all four genres.
83
COHA corpus categorizes data within. The News genre simply also had a number of consecutive
missing observations. It was also excluded, leaving only the Fiction, Nonfiction and Magazine
genres available. Gaps in these were populated using a modified three-period moving average
method.
Finally, comparing both yearly and decennial data from COHA to corpus data for native
speakers of English who were not native to the U.S. could yield interesting results, which could
lead to a number of interesting general observations. Two such non-U.S. corpora would be the
British National Corpus (BNC) and the corpus of British Academic Written English (BAWE)
(Gardner & Nesi, 2012). The BNC corpus, described as synchronic, contains Fiction and
Nonfiction data with intermittent coverage for years from 1960 to 1994. Data examined in this
analysis spans from 1971 to 1994, including the entire decade of the 1980s (see Table 27 on page
179). According to the BNC User Reference Guide, the BNC corpus has a number of defining
characteristics. It is described as:
a sample corpus, composed of text samples generally no longer than 45,000 words.
a synchronic corpus: the corpus includes imaginative texts from 1960, informative texts
from 1975.
a general corpus: not specifically restricted to any particular subject field, register or
genre.
a monolingual British English corpus: it comprises text samples which are substantially
the product of speakers of British English.
a mixed corpus: it contains examples of both spoken and written language.
(BNC User Reference Guide)
Although BNC includes both written and spoken data, the latter was not considered in this
analysis.
84
Similarly, the BAWE corpus contains synchronic data consisting of “proficient
university-level student writing at the turn of the 21st century” (Coventry University, 2013):
[BAWE] contains just under 3,000 good-standard student assignments (6,506,995
words). Holdings are fairly evenly distributed across four broad disciplinary areas
(Arts and Humanities, Social Sciences, Life Sciences and Physical Sciences) and
across four levels of study (undergraduate and taught masters level).
Coventry University (2013)
The BAWE Corpus contains some limited contextual information about the authors, including
self-reported first language and educational background (secondary level). It should be noted that
such student writings differ from both the BNC and COHA in important ways, as the latter two
contain texts by professional authors that have been proofread by professional editors.
4.2. Genre-specific trends for each variety
COHA corpus data reveals a steady, significant decline for all three varieties in almost
every period covered, as seen in Figure 3:
CORR
CONT
ALT
0.0
0.1
0.2
0.3CORR
CONT
ALT
CORR 0.331 0.308 0.279 0.27 0.247 0.281 0.219 0.209 0.185 0.193 0.166 0.15 0.145 0.123 0.122 0.085 0.055 0.051 0.034
CONT 0.118 0.115 0.081 0.078 0.079 0.073 0.042 0.037 0.039 0.039 0.02 0.027 0.014 0.017 0.012 0.006 0.004 0.004 0.002
ALT 0.036 0.023 0.014 0.015 0.012 0.013 0.007 0.005 0.008 0.004 0.005 0.003 0.002 0.002 0.003 0.000 0.001 0.002 0.000
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 3: Corrective, Contrastive and Alternative per 10k, all genres
85
This figure summarizes Table 29, located on page 181, which presents decennial data for
Corrective, Contrastive and Alternative OTC per 10K words of corpus text, for the period from
1810 through the decade of the 2000s. The nature of this decline in the latter half of the 20th
century can by seen more clearly within genres, and within native speaker groups. For example,
Figure 4 (below), which illustrates portions of Table 33 on page 185, shows the incidence per
10,000 words of the Corrective variety of OTC in the COHA database for the period from the
1820s through the 2000s, for four genres: Fiction, Nonfiction, Magazine and News.15 All four
genres have declined in almost every period throughout the time period covered in the corpus:
NF
FIC
MAG NEWS
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
NF
FIC
MAG
NEWS
NF 0.492 0.408 0.362 0.313 0.323 0.413 0.297 0.207 0.208 0.274 0.232 0.237 0.206 0.187 0.254 0.206 0.180 0.164 0.099
FIC 0.114 0.172 0.158 0.159 0.165 0.181 0.139 0.157 0.120 0.103 0.106 0.084 0.085 0.074 0.060 0.039 0.022 0.030 0.030
MAG 0.664 0.537 0.492 0.473 0.351 0.390 0.330 0.327 0.318 0.319 0.225 0.189 0.208 0.133 0.156 0.113 0.078 0.045 0.023
NEWS 0.610 0.436 0.332 0.238 0.195 0.240 0.215 0.230 0.197 0.212 0.149 0.085 0.024 0.036 0.014
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 4: Corrective OTC, four genres, 1820s–2000s
In relatively recent decades, the most noticeable development is that after the 1950s, usage in the
Nonfiction genre has been consistently and significantly higher than in any other genre. Values
for the other three genres decline until approaching zero. If the Corrective instances of OTC per
10,000 words of text are examined by genre (decennial values, from 1820s to 1000), with the
total word count within each genre used as the denominator, there is clearly a very strong
15 Note that data for the News genre is not present for decades prior to 1860. Also, for each genre and in each decade, the word count used as a denominator when determine the proportion per 10k words is the total word count for that genre, and not for the whole corpus.
86
downward trend in all genres. [Note that data for the News genre is not present for decades prior
to 1860]. Clearly, neither of these facts describes the case in prior decades prior to 1960. Aside
from one outlying value in the 1870s, which found Magazines at 0.390 and Nonfiction at 0.413
tokens per ten thousand words, results for the Magazine genre were consistently higher than for
Nonfiction for the entire century from the 1820s through the 1920s. For the nine decades from
the 1870s through the 1950s, the News genre tracked quite closely with Nonfiction, with the
average difference between their values per 10,000 words at only 2.24% across that period. In
general, Corrective OTC usage rates for both the Magazine and News genres were roughly
comparable to those of Nonfiction from the 1920s through the 1940s. The Magazine genre
dropped permanently below Nonfiction after the 1940s, and News did the same one decade later.
Even within the Nonfiction genre there is a noticeable drop beginning in the 1960s.
Perhaps the biggest drop from that decade is in the News genre. This coincides precisely
with the onset of “New Journalism” or “Creative Journalism” (Sagert, 2007), exemplified by
book-length works such as Truman Capote’s “In Cold Blood” (1966) and Tom Wolfe’s “The
Kandy-Kolored Tangerine-Flake Streamline Baby” (1963), as well as a growing proportion of
the Magazine News subgenre. In this subgenre, the traditional, analytical approach to presenting
news and information was deliberately rejected in favor of a radically more personal, often
colloquial narrative tone. This in turn was a reflection of larger issues in society, as well as
technological advances, as Sagert (2007) explains. The passage is quoted at length, to describe in
detail the wide range of agents and social processes at work within the milieu of the relevant
discourse community:
87
New Journalism put a fresh twist on traditional nonfiction writing by incorporating elements
of fiction writing: using dialogue in a conversational style; listing everyday, mundane
details in the setting; developing characters through the use of third-person point of view
and unique narrative voices; and crafting scenes rather than simply sharing information in a
more linear manner. Some believe that New Journalism rose to prominence during the
1960s and 1970s because a strictly factual recounting could not possibly impart the nuances
of — and passions attached to — the Vietnam War, civil rights, women's lib, and gay rights,
among other events. Journalists increasingly began focusing on emotional truth as much as
—or perhaps even more than—imparting information… Clay Felker, who edited New
Yorker and Esquire magazines, suggested that the immediacy of television created a need
for magazine journalists to bring a fresh style to their writing in order to compete. (p. 154)
The News genre thus preceded Nonfiction into a new phase of reduction in use of OTC, since the
latter genre did not resume a period of decline until a decade later. The drop in the 1960s can also
be seen, for example, by examining Contrastive OTC use within the Nonfiction genre in a more
restricted time range, and decomposing the data into segments of Native versus Non-native
speakers of American English, as in Figure 5:
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
NF NS ContNF NNS Cont
NF NS Cont 0.0673 0.0692 0.0519 0.0677 0.0811 0.0720 0.0563 0.0555 0.0580 0.0173 0.0140 0.0148
NF NNS Cont 0.0423 0.0460 0.1801 0.1068 0.0289 0.1028 0.0268 0.0972 0.0279 0.0081 0.0205 0.0280
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Figure 5: Nonfiction Contrastive OTC, NS/NNS per 10k words, 1880s-1990s
88
Although the Non-native speaker component of the Contrastive OTC per 10k words in the Non-
fiction genre fluctuates rather widely prior to the 1960s before damping down considerably after
that period, the native speaker component holds rather steady for eight decades from the 1880s
until the slope of the curve takes a sudden, sharp turn at the 1960s. Especially in recent decades,
it seems reasonable to link this decrease to a perceived colloquialization of English in the latter
half of the twentieth century (Biber & Finegan, 1989; Mair, 1997; Mair & Hundt, 1997). The
difference in the two data segments – one stable with a single steep drop, and the other highly
variable – can be explained by defining this as a characteristically American phenomenon,
strongly influenced by the social milieu. Social and technological changes within a culture
provoke language change. Language change then clashes with pedagogical practice, creating
tension that ultimately leads to changes in traditional pedagogy as well. The early variability of
the Non-native speaker (NNS) indicates an initial degree of independence from the overall
American trend, although it can be seen that the NNS component shares in the decline in the
1960s as well. The social aspects of language change within the Peircean semiotic model are
discussed at length in section 3.2 beginning on page 65 and especially in section 3.3 beginning
on page 68.
For two genres – Magazines and News – the drop from the first half of the 1800s, at the
beginning of the time frame of the corpus, to its terminus at the 2000s is dramatic (Figure 6):
89
MAG NEWS
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
MAGNEWS
MAG 0.664 0.537 0.492 0.473 0.351 0.390 0.330 0.327 0.318 0.319 0.225 0.189 0.208 0.133 0.156 0.113 0.078 0.045 0.023
NEWS 0.610 0.436 0.332 0.238 0.195 0.240 0.215 0.230 0.197 0.212 0.149 0.085 0.024 0.036 0.014
1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s
Figure 6: Corrective OTC, Magazine and News, 1820s–2000s
The Magazine genre, for example, loses 98.0% of its volume in the two-century span between its
first observation and its last (0.664 to 0.0234 per ten thousand words). Over a shorter time span,
meanwhile, the News genre sheds 97.8% of its level of observations per 10k words (from 0.610
to 0.014) between 1860 and 2000s. The Fiction and Nonfiction genres fall by 73.19% and 83.2%
respectively (from 0.114 to 0.030, and from 0.492 to 0.099 observations per ten thousand words).
However, the fact that Fiction and Nonfiction display a lesser degree of overall decline,
as distinct from the roughly 98 percent drop shared by Magazine and News, does not indicate
that their behavior through time is similar in any other manner. Instead, when examining the two-
century span of the corpus as a whole, it is the values for the Magazine and Nonfiction genres
that tend to pattern closest together. The similar trend shared by these two genres can be shown
graphically by isolating them, as seen in (Figure 7):
90
NF
MAG
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
NFMAG
NF 0.492 0.408 0.362 0.313 0.323 0.413 0.297 0.207 0.208 0.274 0.232 0.237 0.206 0.187 0.254 0.206 0.180 0.164 0.099
MAG 0.664 0.537 0.492 0.473 0.351 0.390 0.330 0.327 0.318 0.319 0.225 0.189 0.208 0.133 0.156 0.113 0.078 0.045 0.023
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 7: Corrective OTC per 10k, NF & MAG
The graphs follow roughly the same trend line, except for a period from 1870 to 1910, during
which period the Magazine genre holds fairly steady with a gentle decline. The Nonfiction data
is more fluid, rising steeply from the 1860s to a peak in the 1870s, then dropping steeply, well
below the Magazine genre observations, to a trough that lasts until the 1910s (Figure 8):
NF
NEWS
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
NFNEWS
NF 0.492 0.408 0.362 0.313 0.323 0.413 0.297 0.207 0.208 0.274 0.232 0.237 0.206 0.187 0.254 0.206 0.180 0.164 0.099
NEWS 0.610 0.436 0.332 0.238 0.195 0.240 0.215 0.230 0.197 0.212 0.149 0.085 0.024 0.036 0.014
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 8: Corrective OTC, NF & News per 10k
91
Meanwhile, the News genre follows an interesting course in that in the eight decade period from
1870 until 1950 it patterns even more tightly with Nonfiction than does the Magazine genre.
After 1950, data in the News genre separates from Nonfiction by falling at a steeper rate, and
reaches a low level equivalent to that of Fiction in the 1980s. In the subsequent decade (1990)
the Magazine genre also falls to an equivalently low level. At that time, Nonfiction stands alone
above the other three, as it does for every decade after 1960. As it stands at the present time,
then, there is a considerably higher incidence of Corrective OTC in the Nonfiction genre than in
any of the other three. From the 1820s through 2000s the Magazine genre displays a downward
trend similar to the other three genres.
Finally, the Fiction genre stands in some respects apart from the other three. Although it
also displays a clear downward trend over the whole period, its period-to-period rate of change is
a relatively gradual one. In the century and a half from 1820s until 1980, which constitutes the
majority of the time frame of the sample, the Fiction genre consistently displays the lowest
values as well as the lowest variability.
There are a total of 225 observations of Alternative OTC in the two century span of the
COHA data. However, the decline of this variety has been the most pronounced of all three
varieties. As opposed to the long, gliding decline of other forms, Alternative OTC underwent a
notably steep plummet (Figure 9):
92
ALT
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
ALT
ALT 0.0332 0.0218 0.0137 0.014 0.0117 0.0124 0.0059 0.0048 0.0072 0.0044 0.0032 0.0033 0.0017 0.0012 0.0017 0.0004 0.0008 0.0011 0.0003
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 9: Alternative OTC per 10k, decennial data, 1820s–2000s
This result, however, should be tempered by recognition of the broader perspective within the
data. The maximum value on the scale of this graph is lower than those of other graphs. Even
since the earliest periods of the data, the level of usage of Alternative OTC was never more than
a fairly small fraction of that of the two more common varieties. The historical trends of all three
are presented numerically in Table 29 (on page 181) and shown drawn at the same scale in
Figure 3 (on page 85).
The overall trend in Alternative OTC use can further be decomposed into genre-specific
data, as show in Figure 10:
93
NF Alt
FIC ALT
MAG ALT
NEWS ALT
0.000
0.010
0.020
0.030
0.040
0.050
0.060
0.070
0.080
NF AltFIC ALTMAG ALTNEWS ALT
NF Alt 0.05480.05600.03020.03800.03450.02830.00310.00600.01690.01130.01180.01300.00650.00970.00640.00340.00640.00640.0000
FIC ALT 0.00530.00400.00680.00330.00320.00490.00180.00360.00500.00170.00080.00000.00000.00000.00000.00000.00000.00000.0007
MAG ALT 0.07580.03180.01410.01900.01580.01800.01340.00850.00590.00530.00340.00510.00360.00000.00350.00000.00000.00000.0000
NEWS ALT 0.00000.01980.02270.00000.00700.00710.00290.00290.00000.00000.00000.00000.00000.00250.0000
1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s
Figure 10: Alternative OTC by genre
In general, the data for the Fiction genre is always lower than that of other genres, and
completely unmarked by dramatic spikes or drops. With the exception of a few outlier
observations (particularly between 1870 and 1890), the Nonfiction genre sees the highest rate of
incidence across the time span of the corpus. This closely parallels the data for the other two
varieties, as discussed next.
Figure 11, Figure 12, Figure 13 and Figure 14 (beginning on page 95, below) present an
overview of the larger trends in the data by comparing decennial figures compiled from COHA
corpus data for the two major varieties of OTC (Corrective and Contrastive) for each of the four
genres (Nonfiction, Fiction, News and Magazine, respectively) in the period from the 1820s
through the 2000s. To present the four genres in relative proportion to one another, the vertical
axes are held to the same scale in each figure. Each figure shows instances per 10,000 words of
text. Overall, the data shows a high degree of consistency across genres, displaying a few broad
trends:
94
For both of the two major varieties of OTC, there is an overall downward trend throughout
the two centuries of the data, with occasional periods of relative stability. The longest stable
period occurs for the Corrective variety in the Nonfiction genre, hovering at or somewhat
above 0.2 instances per 10,000 words roughly from the decade of the 1890s through the
1970s. One could arguably include the decade of the 1980s in this period as well, but that
decade seems more properly placed in the period of gradual decline that occurs after the
1970s.
For three of the four genres – Fiction, Magazine and News – all varieties of OTC have fallen
into relatively rare use. The notable exception is found in use of Corrective OTC in the
Nonfiction genre.
Across all genres and for most of the time span of the corpus, Corrective OTC is at least
twice as common as Contrastive, and in some cases as much as five times more common.
The Nonfiction genre, as mentioned above, is notable as the genre which has retained the highest
usage rates Corrective OTC to the present day (Figure 11):
CORR NF
CONT NF
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CORR NF
CONT NF
CORR NF 0.492 0.408 0.362 0.313 0.323 0.413 0.297 0.207 0.208 0.274 0.232 0.237 0.206 0.187 0.254 0.206 0.180 0.164 0.099
CONT NF 0.130 0.250 0.112 0.117 0.127 0.173 0.058 0.060 0.098 0.079 0.064 0.081 0.052 0.080 0.047 0.013 0.016 0.025 0.009
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 11: Nonfiction genre, Corrective versus Contrastive OTC, 1820s-2000s
95
The Fiction genre is notable not only for low rates of use of both Corrective and Contrastive
OTC across the entire span of the corpus, but also for a very flat rate of decline. This point comes
up repeatedly in this analysis, including as a key point in the section 4.7, which explores the
dynamics of relationships between the various discourse particles, with the larger goal of
providing supporting evidence for the assertion of polysemy in OTC. Data for the Fiction genre
is displayed below (Figure 12):
CORR FIC
CONT FIC
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CORR FICCONT FIC
CORR FIC 0.114 0.172 0.158 0.159 0.165 0.181 0.139 0.157 0.120 0.103 0.106 0.084 0.085 0.074 0.060 0.039 0.022 0.030 0.030
CONT FIC 0.029 0.039 0.058 0.031 0.034 0.031 0.023 0.016 0.020 0.019 0.007 0.016 0.005 0.005 0.001 0.005 0.000 0.001 0.000
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 12: Fiction genre, Corrective versus Contrastive OTC, 1820s-2000s
96
The News genre (Figure 13) is distinctive primarily for its two steep drops in Corrective OTC
usage, the first beginning in the 1860s or 1870s, and the second beginning after the 1950s. The
latter coincides precisely with the onset of “New Journalism” or “Creative Journalism” (Sagert,
2007), and is almost certainly a direct result of that social trend in the discourse community
relevant to the News genre:
CORR NEWS
CONT NEWS
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CORR NEWSCONT NEWS
CORR NEWS 0.610 0.436 0.332 0.238 0.195 0.240 0.215 0.230 0.197 0.212 0.149 0.085 0.024 0.036 0.014
CONT NEWS 0.190 0.079 0.037 0.043 0.007 0.014 0.002 0.014 0.008 0.005 0.005 0.000 0.004 0.000 0.000
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 13: News genre, Corrective versus Contrastive OTC, 1860s-2000s
97
The Magazine genre shares many similarities with the News genre. Perhaps the most apparent
distinction is that Corrective OTC use in the Magazine genre began its initial twentieth-century
decline in the 1920s, decades earlier than that within the News genre. However, a secondary
period of downward pressure is also observed beginning in the 1970s (Figure 14):
CORR MAG
CONT MAG
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CORR MAGCONT MAG
CORR MAG 0.664 0.537 0.492 0.473 0.351 0.390 0.330 0.327 0.318 0.319 0.225 0.189 0.208 0.133 0.156 0.113 0.078 0.045 0.023
CONT MAG 0.303 0.168 0.104 0.149 0.135 0.105 0.078 0.066 0.051 0.059 0.032 0.027 0.014 0.012 0.016 0.007 0.001 0.001 0.002
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 14: Magazine genre, Corrective versus Contrastive OTC, 1820s-2000s
These similarities and differences present an interesting dynamic that could be explored in future
analyses.
The genre-specific historical data for Contrastive OTC displays several trends in common
with that of the Corrective variety, but also some significant differences. Similar to Corrective,
all genres displayed a higher frequency of usage throughout the nineteenth century than the
twentieth, and all have been fairly steadily falling throughout the time period covered by the
corpus, as exemplified by data for the Nonfiction and Magazine genres (Figure 15, below):
98
CONT MAG
CONT NF
0.0
0.1
0.2
0.3 CONT MAGCONT NF
CONT MAG 0.303 0.168 0.104 0.149 0.135 0.105 0.078 0.066 0.051 0.059 0.032 0.027 0.014 0.012 0.016 0.007 0.001 0.001 0.002
CONT NF 0.130 0.250 0.112 0.117 0.127 0.173 0.058 0.060 0.098 0.079 0.064 0.081 0.052 0.080 0.047 0.013 0.016 0.025 0.009
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 15: Contrastive OTC per 10k words, MAG & NF, 1820s–2000s
Also similar is the strong association with the Nonfiction genre throughout most periods of the
data, and the fact that Contrastive OTC seems to have fallen into relative disuse in all genres
other than Nonfiction. The differences are basically in the degree that these trends hold true. For
example, while the dominance of Nonfiction among genres in the Corrective OTC data did not
begin until the 1960s, Nonfiction has been dominant for the Contrastive variety since at least the
first decade of the twentieth century (1900s). Moreover, while Corrective OTC still has
measurable values across every genre, for Contrastive, the values per 10,000 words are quite low
for all genres aside from Nonfiction, and have been that way since the 1980s.
99
Meanwhile, the relevant graphs for in/by contrast (see Figure 23: Contrastive OTC,
clause-initial In/by contrast 1830s–2000s on page 126) rise sharply. Intuitively, a comparison of
these two graphs would not suggest any degree substitutive relationship between Contrastive
OTC and in/by contrast within the Fiction genre of COHA corpus data. This is unsurprising for a
number of reasons. Texts within the Nonfiction genre, by their nature, are relatively heavily
invested in examining and explaining the relationships between various facts and propositions.
Discourse particles such as Contrastive OTC, in contrast and by contrast are specifically tailored
for such comparisons, leading to high usage rates. Texts within the Fiction genre are more
invested exploring personal relationships, dramatic situations, and so on. The relative lack of
verbiage within the Fiction genre devoted to organizing and advancing logical arguments
suggests that these texts should rely less heavily on the contrastive discourse particles than would
a Nonfiction text. This point will become important in the vector autoregression analyses in
section 4.7, beginning on page 128.
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4.3. Syntactic structure as a test of polysemy of OTC
Sentential placement is an important element of the cognitive-semiotic ground, as
discussed previously, sending a strong signal regarding the nature of the schema that is being
presented to the hearer/reader. More importantly to this discussion, Bolinger’s oft-cited (1968)
assertion that “a difference in syntactic form always spells a difference in meaning” (p. 127) and
the related statement by Harris (1970) that “difference of meaning correlates with difference of
distribution” (p. 785) suggest that syntactic structure can be taken as one element in a series of
tests of polysemy for OTC. As will be seen, syntactic structure presents strong evidence in favor
of a polysemous reading. Finally, a more general summary of the results of this section is a key
feature of Section 5 (“Pedagogical approach”) beginning on page 151.
Typically placed between clauses, between bracketing commas, or in sentence-initial
position, OTC is generally detachable from the syntactic structure of the sentence (Brinton, 1996,
p. 34). Sentence-initial position, or placement between two clauses of a compound sentence, is
very strongly correlated with Corrective OTC and the attendant assertion that P1 is categorically
false (see Table 1 on page 16). A token of OTC that follows a noun phrase or a prepositional
phrase tends to be used for Contrastive OTC, and thus to convey the proposition that the referent
of P2 is categorically different from that of P1 (Table 5: Examples of Contrastive OTC, page 23).
Finally, Alternative OTC is typically sentence-medial, following the coordinating conjunction
“or” or subordinator “if” (Table 7: Examples of Alternative OTC, page 32).
Syntactically, three structures stood out as comprising clear distinctions between the
Corrective and Contrastive varieties. Certainly the most robust of these was that the corpus data
was for all practical purposes utterly devoid tokens of Corrective OTC located after a
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prepositional phrase. In nearly two centuries of U.S. English data, only two instances of post-PP
corrective OTC were identified (Table 12):
Table 12: Example of post-PP Corrective OTC (COHA), NS/NNS
Year Genre Text Speaker
1823 FIC
Thus the existence of the Egyptian does not depend on the brightness of the
sun, or the quantity of rain. For him, on the contrary, there exist only those
perfectly simple conditions, which form the basis of hisNNS
1871 NF
Dr. Bennett does not draw from his experiments the conclusion to which they
so obviously point. On them, on the contrary, he founds a defence of the
doctrine of spontaneous generation, and a general theory of spontaneous
development.
NNS
The source for the first token was a modern U.S. English translation of Hegel’s 1837 Lectures on
the Philosophy of History, so it can readily be excluded from any account of native U.S. English
usage. The second is from an 1871 Nonfiction work by the British physicist John Tyndall
Second clearest among the syntactic features distinguishing the two varieties is the rarity
of sentence-initial Contrastive OTC in U.S. usage. A total of only 19 instances in the two
hundred year span of the COHA corpus were sentence-initial, of which the first 11 instances, or
58% of total, were written during the 19th century. Moreover, six of these, or nearly one-third,
were written by speakers of non-U.S. English (Table 13):
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Table 13: Examples of sentence-initial Contrastive OTC (COHA), NS/NNS
Year Genre Text Author
1823 FIC is -- yet, it is more improbable, that it should be a mere invention. On the contrary, we are apt to be suspicious of a very plausible, NS
1824 MAGlanding in boats is impracticable. It has no good watering place. On the contrary, Mesurado Cape is a considerable eminence of land jutting into the sea, high
NS
1831 MAG may even now be found, to test the truth of Fielding's descriptions. On the contrary, Richardson, though he dealt much more with the human heart and NNS
1835 NF most admire; and perhaps it may in certain cases be applied to every one. On the contrary, if we observe narrowly less gifted beings, we find them not so NNS
1839 FIC for me to profit by the night, and eventually to effect my escape. "On the contrary, if I endeavoured to make my way through the mist which NS
1841 NF he merits the thanks rather than the esteem of mankind. On the contrary, we find in the writings of Rousseau and Abbe Raynal, a NS
1858 NF in wonderful exuberance ready to be laid hold of by their greedy hands. On the contrary nobody has ever expected me to be president. In my poor, lean, NS
1870 FIC the evening, and the Federal forces never made another charge. On the contrary, the Confederate lines everywhere advanced. Longstreet swept NS
1871 FIC they seemed to have something of a friendship for each other. On the contrary, Mrs. Hodge seemed to have less and less regard for her boarder NS
1872 NF great lexicographer had breathed his last only a few hours before. On the contrary, small and ungenerous minds can not admire heartily. To their NNS
1874 MAG as compared with an average of $247,459 of exports. On the contrary, in two years of Houston's second term, 1843-1844, when NS
1917 NF the operation aimless. Therefore White is condemned to inactivity. On the contrary, Black's line of action is clear. His entry on the NNS
1923 NF This is a trait which persisted throughout the ceramic history of Peru. On the contrary, in ancient southern Mexico and Central America, habit ran in NS
1948 NF far as they could, by force, cajolery, or sufferance, much like Gypsies. On the contrary, the Negritos are sturdily independent; so that the cases are NS
1948 NF and their products from the islands bordering or within the Pacific Ocean. On the contrary, other commodities have an unusually wide range of production. NNS
1954 MAG like this we expect wit, or individuality, or both. On the contrary, the conversation is thick with cliches and the speech of the NNS
1957 FIC that he was right, quite right. He just wanted Arthur Winner to know. On the contrary, on the other hand, shut up in his room, Noah might have been NS
1977 NF leaping fresh. There was never any question of using stale fish. On the contrary, it is today's produce that needs masking. NS
1998 MAG "ventilated" with ambient air blown through the hollow honeycombs. On the contrary, thick, zero-expansion glass mirrors retain heat much longer. NS
This tendency is a straightforward complement to the corresponding tendency for sentence-initial
OTC to occur within Corrective schema. After the turn of the century, no more than two
instances of sentence-initial Contrastive OTC are found in any decade, while two separate
decades of the 20th century contain zero tokens apiece.
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Third, a total of only 50 instance of post-NP (where the NP is clause-initial) Corrective
OTC were identified in the entire span of the COHA data, with the raw count of tokens per
decade exceeding 10 in only one decade, and greater than 5 in only the four contiguous decades
from the 1820s through the 1850s (Figure 16):
0
2
4
6
8
10
12
post-NP
post-NP 1 6 7 11 6 2 4 0 3 3 1 0 2 1 2 1 0 0 0 0
1810s 1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s
Figure 16: Post-NP Corrective OTC, COHA 1810-2000 (raw data)
One case in which post-NP Corrective OTC is employed is when the referent of P1 is simply
repeated, as in the example below. This repetition satisfies the speaker/writer’s communicative
goal of vividly underscoring the identity of the referent, to make it particularly salient. The
(identical) referents of P1 and P2 are italicized, while OTC is again in bold and underlined in
example (37), from a token in the Magazine genre from the year 1841:
(37) The policy of nations has determined that it is not expedient to allow individual
impunity to certain acts, even though they should he done under public direction,
and accordingly the public abstains from directing them. The policy of nations, on
the contrary, has agreed with the dictate of humanity in according individual
impunity to public military operations, carried on under the authority of
governments…
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Though this example is framed with OTC occurring post-NP, the syntactic form typical for a
Contrastive schema, it is a straightforward instance of Corrective OTC. The veridicality of P1 is
assigned as negative via the negation of the adjective “expedient.” The rarity of this form in
recent decades is striking: there are zero tokens of post-NP Corrective OTC in the four decades
since the 1960s, and only four examples in the three-decade period running from the 1940s
through the 1960s (Table 14). Recall that the NS/NNS distinction refers to speakers of U.S.
English versus those of all other linguistic backgrounds:
Table 14: Examples of post-NP Corrective OTC (COHA), NS/NNS, 1940–Present
Year Genre Text Speaker
1942 FIC
It showed that the novels were wrong; that commonsense was right; that
happy marriages were not made from great love. Great love, on the contrary,
could not possibly be fitted into such a chic apartment as this; it would knock
over all the little tables.
NS
1956 FIC
Climatologically, this day of which I write was rare for Dorchester County, rare
for the Eastern Shore, where the same ubiquitous waters that moderate the
temperatures – the ocean, the Bay, the infinite estuaries, creeks, coves, guts,
marshes, and inlets – also make them quite uncomfortable. This day, on the contrary, was excessively warm (the temperature as I walked to the hotel
must have reached 95), but extremely dry
NS
1959 NF
It categorically refuses to admit that the offspring of such a union could be the
Divine Child, born to inaugurate a new era of peace and concord: Slim's child,
on the contrary, is seen as inevitably tainted by her heredity.NNS
1966 FIC
I have talked to his partners and gone over the accounts of his firm, and I have
not been able to disprove this. Everything, on the contrary, tends to confirm
the bracketing of his initial crime with the failure of his attempted marital
reconciliation…
NNS
Despite the genuine appeal of one example from the Fiction genre in 1942, as given in Table 14
above, for all practical purposes the presence of a meager seven tokens in the past century is
sufficient evidence from corpus data to draw the conclusion that this structure is so weakly
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attested that removing it from the set of forms that should be considered available for use by
second language learners is an acceptable practice.
A total of 21 of these tokens were from the Magazine genre, three were from News, and
the remaining 26 were split evenly between Fiction and Nonfiction. Because of the relatively
large proportion of data from the News and (especially) Magazine genres, for which no
autobiographical data is available, no conclusions could be reliably drawn regarding usage of
post-NP Corrective OTC by native versus non-native speakers of U.S. English. Perhaps most
striking is the relatively large proportion of these tokens in the Magazine genre in the 1840s,
which was the chief source of the relative spike in that decade (Table 15):
Table 15: Post-NP Corrective OTC by genre, 1810s–1960s
DEC TOT MAG NEWS FIC NF1810s 1 0 0 0 11820s 6 3 0 0 31830s 7 1 0 2 41840s 11 8 0 2 11850s 6 2 0 4 01860s 2 1 0 0 11870s 4 1 1 0 21880s 0 0 0 0 01890s 3 1 1 1 01900s 3 1 1 1 01910s 1 1 0 0 01920s 0 0 0 0 01930s 2 2 0 0 01940s 1 0 0 1 01950s 3 0 0 1 21960s 1 0 0 1 0
In general, however, the scant evidence available does not suggest any large difference in the
percentage of tokens generated by the two groups in any decade.
Just as the particle OTC is heavily slanted in favor of the Corrective variety, sentence-
initial sentential position very strongly selects for Corrective OTC (Table 16):
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Table 16: Sentence-initial Corrective structuresCorrectiveOn the contrary, 3600On the contrary 260On the contrary - 22On the contrary: 18On the contrary; 9
3909
This table is an excerpt from Table 18, on page 111. Here the total of 3,909 sentence-initial
tokens of Corrective OTC comprises 59.82% of all tokens of Corrective OTC and 48.09% of the
grand total of all tokens of all three varieties of OTC. Sentence-medial position bracketed by
commas is the second most frequent instantiation of Corrective OTC. These are uniformly post-
VP, as illustrated in example (38):
(38) Our case studies suggest, on the contrary, that Muslims borrowed heavily on
interest, lent money on interest, and did not hesitate to liquidate the estates of fellow
Muslims if owed money.
By subtracting the total of post-NP Correctives (50) and post-PP Correctives (2) from the total of
736 sentence-medial Corrective OTC (bracketed by commas), we obtain a remainder of 684
instances.
As a further indication of the correlation between sentential placement and specific varieties
of OTC, an overwhelming majority of the 1,352 instances of Contrastive OTC in the COHA
corpus data are sentence-medial, and a similarly robust majority of those sentence-medial
instances are found in post-NP position (Table 17):
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Table 17: Sentence-medial Contrastive OTCContrastive, on the contrary, 1208 on the contrary 53while, on the contrary 26 on the contrary, 16while on the contrary 8, on the contrary 7contrary 5when, on the contrary 4on the contrary. 2- on the contrary, 1that on the contrary 1: on the contrary, 1but, on the contrary 1
1333
As Table 17 (above) indicates, the syntactic correlation between the syntactic form and its
semantic import is particularly clear and obvious. After the most common form, sentence-medial
surrounded by commas, the second most frequent form is sentence-medial OTC that is not set off
by punctuation in any manner, with 53 instances in the data. The most common form thus occurs
nearly 23 times more frequently than the second-most, a drop-off that is particularly definitive of
the Contrastive variety of OTC.
Typical instances of sentence-medial Contrastive OTC, taken from recent texts, can be
found in examples (39) and (40) below. The first is an example of Contrastive OTC, post-NP:
(39) … distance from comic objects is also implied in the very nature of loud
laughter,which requires an anesthsie momentane du Coeur. Aesthetic
illusion, on the contrary, has an affinity with emotional involvement,
and this in turn correlates with seriousness, as can also be seen in drama.
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The second is an example of Contrastive OTC, post-PP:
(40) For in Germany the innovations in administrative technique, in machine
production, and the rest… had been prepared by the gradual training of
generations of former medieval clerks and craftsmen in more and more
technically-advanced ways. In Egypt, on the contrary, the same events tended to
destroy what craftsmen's skill and what intellectual soundness had in fact existed
there in the eighteenth century.
Specifically, among 1,333 instances of sentence-medial Contrastive OTC, a total of 1,157 (or
85.58% of the grand total) are post-NP, while 176 (13.02%) are post-PP (Table 17):
Here it is worth noting that a striking parallel can be drawn between these results
concerning Contrastive OTC and those within Bell’s (2004) discussion of the discourse
marker/particle on the other hand (OTOH). Bell observed that OTOH has three distinct uses: as a
listing marker, a contrastive marker and a cancellative marker (p. 2179). Bell’s (2004)
observations regarding the correlation between post-NP sentential placement and the contrastive
variety of OTOH bear a striking resemblance to the data for Contrastive OTC:
...contrastive [non-correlative on the other hand] is more likely to appear in the post-subject
NP medial position... two particular elements are being compared and NC-OTOH operates
as a focus marker in the post-subject NP position to highlight the preceding NP as the item
that is being compared to an equivalent item in the prior discourse. (pp. 2180–2181)
Here the property of focus clearly interacts with the selection of syntactic form and the related
semantic scheme to frame a contrastive information structure (Molnár, 2002). However, as
variants A and B of constructed Contrastive OTC example (41) reveal, those choices can be
independent of the contextual factors within the conceptual ground (ground 2) of OTC:
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(41) Dark clouds run along the western rim of the sky.
A: The clouds in the east, on the contrary, are white and fluffy.
B: In the east, on the contrary, the clouds are white and fluffy
This present analysis would suggest instead that the discourse particle OTOH is polysemous, and
that the post-NP sentential placement is one element of ground 1 for the Contrastive variety of
that particle. The robust tendency for discourse particles in post-NP placement to consistently
fulfill a Contrastive role is one key finding of this research.
A summary of the raw count of the tokens within the COHA corpus for a number of
syntactic structures for each variety is presented below (Table 18):
110
Table 18: Raw count of syntactic structures, all three varieties, COHA 1810–2000
Corrective OTCOn the contrary, 3600, on the contrary, 736; on the contrary, 702but, on the contrary 361On the contrary 260but on the contrary 259On the contrary. 110 on the contrary 88; on the contrary 63: on the contrary, 53 on the contrary, 36that on the contrary 34, on the contrary 32- on the contrary, 26But, on the contrary 23contrary 23On the contrary - 22On the contrary: 18on the contrary. 17- on the contrary 14But on the contrary 14On the contrary! 13On the contrary; 9when, on the contrary 5: on the contrary 4while, on the contrary 3And, on the contrary 3if, on the contrary 2when on the contrary 2if on the contrary 1When, on the contrary 1And on the contrary 1TOTAL 6535
Contrastive OTC, on the contrary, 1208 on the contrary 53while, on the contrary 26
On the contrary, 23 on the contrary, 16When, on the contrary 12
while on the contrary 8
, on the contrary 7And, on the contrary 5
contrary 5when, on the contrary 4
But, on the contrary 4on the contrary. 2- on the contrary, 1that on the contrary 1On the contrary 1: on the contrary, 1While, on the contrary 1
When on the contrary 1
And on the contrary 1but, on the contrary 1TOTAL 1381
Alternative OTCIf, on the contrary 103if, on the contrary 40, on the contrary, 37If on the contrary 12 on the contrary 5On the contrary, 4if on the contrary 4; on the contrary, 2, on the contrary 1when, on the contrary 1
When on the contrary 1
but, on the contrary 1 on the contrary, 1TOTAL 212
To investigate which structure(s) among those used for the Nonfiction variety of Corrective
OTC are preferred above others, this analysis examined a total of 39 syntactic structures across
the entire time frame of the COHA corpus. Only 27 of these were found in use for Corrective
OTC in the Nonfiction genre. Moreover, the majority of those occurred with only minimal
frequency, especially in the 20th century. The chart below only shows the five that occurred most
frequently for Nonfiction Corrective OTC (Figure 17):
111
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
0.180
0.200On the contrary,
, on the contrary,
; on the contrary,
but, on the contrary
but on the contrary
On the contrary, 0.150 0.108 0.134 0.136 0.072 0.042 0.125 0.066 0.053 0.104 0.094 0.071 0.108 0.058 0.136 0.050 0.048 0.093 0.041
, on the contrary, 0.082 0.059 0.054 0.047 0.062 0.070 0.042 0.018 0.028 0.028 0.032 0.016 0.016 0.025 0.022 0.030 0.016 0.012 0.012
; on the contrary, 0.054 0.056 0.052 0.038 0.037 0.084 0.046 0.018 0.031 0.036 0.017 0.022 0.009 0.029 0.022 0.027 0.029 0.003 0.000
but, on the contrary 0.061 0.042 0.030 0.031 0.034 0.031 0.021 0.021 0.016 0.011 0.008 0.013 0.009 0.009 0.003 0.006 0.016 0.003 0.003
but on the contrary 0.020 0.029 0.019 0.012 0.020 0.014 0.006 0.006 0.005 0.008 0.014 0.006 0.009 0.006 0.022 0.003 0.003 0.000 0.000
1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s
Figure 17: Nonfiction Corrective OTC, 5 most common syntactic structures per 10k
Clearly, aside from an anomalous period in the 1870s (which may be an artifact of the corpus
itself), one structure stands above the others: sentence-initial OTC, followed by a comma (i.e.,
“On the contrary, proposition 2”). This structure apparently managed in the end to defy the
downward pull that has led other structures to their minima – or more accurately, its floor seems
to be holding relatively well above zero, at 0.04 per 10,000 words. That value held within both
the 1870s and the 2000s, and in the period between those decades the data bounced between that
floor and a ceiling of 0.14 per ten thousand. All other structures occur at a frequency of less than
0.04 instances per 10,000 words in every decade subsequent to the 1880s, and less than 0.02
instances per 10,000 words since the 1980s. However, the sentence-medial form that is bracketed
by commas (i.e., “P1, on the contrary, P2”) also seems to be maintaining a floor above zero,
albeit at only 0.01 per ten thousand. All the other syntactic structures are at or near zero instances
per 10k words, though each may still occur from time to time. In short, although all syntactic
forms of OTC have declined through time, they have done so at different rates, and at least two
of these structures show evidence of finding a stable rate of usage. This leaves open the
112
possibility that the overall decline in Corrective OTC’s usage in Nonfiction is in large part the
result of pruning away of less-favored syntactic variants while sustaining the use of preferred
ones. In the future, this process may culminate with one or two structures remaining in use, while
all other structures will be more or less abandoned. The similarity between this process and the
overall semantic narrowing of OTC through time is a matter that seems to warrant further
investigation, which is left for a future analysis.
4.4. Impact of U.S. versus non-U.S. varieties of English
After extensive research, native language variety was identified for more than 5,500
authors in the Fiction and Nonfiction genres. Author data was unavailable for the News and
Magazine genres. Data-gathering regarding the native/nonnative speaker status of the Fiction and
Nonfiction genres required several months of intensive investigation. This process included
gathering biographical data from obituaries, topic-domain nonfiction texts such as literary
criticism, and various online sources such as Wikipedia, among other sources. Not infrequently,
researching a given individual author required an extended amount of time and labor to uncover
relevant biographical data (if available).
Granger and Tyson (1996) argue that French learners' overuse and misuse of on the contrary
is probably due to an over-referent of the semantic properties of French au contraire, which can
be used to express both concessive and antithetic relationships. While their qualitative
characterization of these instances as “errors” may need to be reconsidered in the light of this
present analysis (see the discussion of example (19) on page 26), their quantitative results
certainly hold true. Perhaps related to the quantitative findings of Granger and Tyson, the use of
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OTC among these three forms has distinctive usage patterns among native speakers of U.S.
English (NS) as opposed to non-native speakers of U.S. English (NNS).
Within the Fiction and Nonfiction genres, each token was investigated to determine
whether or not the author is or was a native speaker of U.S. English. This analysis examined a
data set of 2,274 textual extracts containing Contrastive OTC and in/by contrast obtained from
COHA, and explored the manner in which these particles have evolved through time, as well as
the respective manner in which they are utilized by native and nonnative speakers of U.S.
English. It is worth noting that any reference to “native speaker” (NS) within this analysis is
made in the context of native speakers of U.S. English, rather than the far more common context
of “native speakers of English.” This is largely an unavoidable outcome of the U.S.-centric
nature of the COHA corpus, but also fulfills a very specific and narrow research goal of ensuring
maximum parsimony in the data set. It does not reflect any attempt to approach the data with any
pre-existing value judgment. The term “native speaker” is admittedly imprecise and subjective in
some cases (as will soon be seen in the example of George Santayana). With that caveat, it is
employed with respect to any individual who spent his or her formative years learning English
within the U.S. Those writers whose linguistic background was rooted in some language other
than English, or in other “Inner circle” English language communities (e,g., Great Britain and
Canada, see Kachru, 1985; Quirk et al., 1972) are collectively counted as non-native speakers
(NNS) of U.S. English. Synchronic data from BAWE and BNC suggests that U.S. usage of OTC
is currently measurably different from that of native speakers of British English, and this
information is noted whenever it is relevant to the research questions at hand. In-depth
exploration of issues revolving around the English variety of authors is left for later analyses.
114
Note that if there is any quantitative bias in the statistical analyses, it should be a bias
against results that indicate a NS-NNS dichotomy. To put that another way: finding a distinction
should be mildly surprising. That is because the COHA database gathers data from U.S.
publications edited by professionals whose area of expertise lies in replacing grammatical
structures considered non-standard with more standard formulations, in the context of a
particular genre and discourse community. This editing process would reduce the overall
incidence of non-U.S. English writers adding forms contrary to tendencies prevalent in U.S.
practice in the relevant time period. Despite the moderating influence of such editors, the
dichotomy is still clear. Recall again that the distinction between NS and NNS is defined here as
that between native and non-native speakers of U.S. English rather than merely of English
(Figure 18):
CONTR NF
NF NNS
NF NS 0.000
0.050
0.100
0.150
0.200
0.250
0.300
CONTR NF NF NNS NF NS
CONTR NF 0.13 0.25 0.11 0.11 0.12 0.17 0.05 0.06 0.09 0.07 0.06 0.08 0.05 0.08 0.04 0.01 0.01 0.02 0.00
NF NNS 0.06 0.19 0.06 0.06 0.08 0.15 0.01 0.01 0.05 0.03 0.00 0.03 0.00 0.03 0.00 0.00 0.00 0.00 0.00
NF NS 0.06 0.04 0.04 0.05 0.03 0.02 0.04 0.04 0.03 0.04 0.05 0.04 0.04 0.03 0.03 0.01 0.00 0.00 0.00
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 18: Native/non-native speakers, Contrastive OTC, Non-fiction
The variability in overall Contrastive OTC throughout 150 years (from the 1820s through the
1960s) is almost entirely due to variability in the non-native speaker data; from the 1850s
through the 1870s the trend in the native speaker data runs directly counter to the overall trend,
115
since its impact is outweighed by that of the NNS. This finding is buttressed by a simple listing
of all Nonfiction works with more than 6 instances of Contrastive OTC. The only author listed as
a native speaker of U.S. English is the eminent George Santayana, and a cursory perusal of his
biography suggests that his linguistic background was enriched by considerable international
influence, as he lived in Spain from his birth until he was five years old (Table 19):
Table 19: Nonfiction texts with most instances of Contrastive OTC, NS/NNS
Year Author NS/NNS TitleContr.
OTC
1824 A. H. L. Heeren NNS Reflections On Politics 7
1835 Mrs Albertine-Adrienne Necker de Saussure; NNS Progressive Education 8
1836 Bernardin de Saint-Pierre NNS St Pierre’s Studies 12
1837 Georg Wilhelm Friedrich Hegel NNS Philosophy of History 14
1848 Alexander von Humboldt NNS Personal Narrative 11
1862 Henri Jomini NNS The Art of War 11
1874 Paul Lacroix (pseud. P. L. Jacob)
NNS Manners & Customs 12
1900 Joseph Deniker NNS Races of Man 7
1950 George Santayana NS Dominations & Powers 7
The reasons for the remaining pattern of NNS variability are not discoverable from the corpus
data. However, this result does establish three facts: first, NNS usage is remarkable and
consistently different from NS, and its impact is significant enough to drive the overall trend.
The latter is the case despite the fact that the data was presumably edited by native speakers who
retain the usage that is in earlier decades demurred and in later decades became nonstandard. In
some cases they may do so to retain the stylistic flavor of the original wording. Still other
instances, however, may be due to a lack of strong and clear guidance from native speaker
intuition, perhaps stemming from the shared etymological origins of Corrective and Contrastive
OTC, or perhaps by recurrent exposure to earlier usage. Meanwhile, particularly in the span
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between 1880 and 1960, the value for native speakers hovers fairly tightly near a mean of around
0.04, before taking a relatively steep drop to a value not significantly different from zero. Aside
from that one drop, the trend line is very nearly horizontal. Second, NNS variability drives the
trend before 1870, and NNS usage rates of OTC outweigh native speaker use until then. Third, in
a very surprising development, after 1870 non-native speaker usage rates of Contrastive OTC per
10,000 words drop below native speaker rates (with only one exception), and remain there until
both NS and NNS rates reach a level not significantly different than zero. This period in the data
will be considered again in section 4.7 beginning on page 128.
Up to and including the decade of the 1880s, changes in the rates of use per 10,000 words
of Corrective OTC in the Nonfiction genre also are driven by variation in the NNS data. Recall
again that the distinction between NS and NNS is defined specifically here as that between
native speakers of U.S. English, rather than merely native speakers of English in general, versus
those of all other linguistic backgrounds (Figure 19):
CORR NF
NF NNS
NF NS
0.000
0.100
0.200
0.300
0.400
0.500
0.600
CORR NF NF NNS NF NS
CORR NF 0.49 0.40 0.36 0.31 0.32 0.41 0.29 0.20 0.20 0.27 0.23 0.23 0.20 0.18 0.25 0.20 0.18 0.16 0.09
NF NNS 0.21 0.13 0.09 0.07 0.16 0.32 0.07 0.07 0.12 0.10 0.05 0.06 0.05 0.07 0.05 0.11 0.10 0.04 0.04
NF NS 0.28 0.25 0.26 0.24 0.16 0.08 0.22 0.12 0.08 0.16 0.16 0.15 0.15 0.10 0.19 0.08 0.07 0.11 0.04
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 19: Native/non-native per 10k, Corrective NF
In particular, an atypical spike in the value for the 1870s is derived solely from a spike in texts
authored by nonnative speakers (NNS), while a spike in native speaker (NS) usage in the 1880s
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is wholly offset by a drop in the NNS component of that time period. It would seem that the
period around 1860–1880 was one of considerable adjustment and fluctuation. Subsequent to the
1880s, however, both NS and NNS data remain relatively flat, with NNS rates of usage
consistently below those of NS. Again it is interesting to note that the spikes in both Corrective
and Contrastive OTC usage rates in the 1870s applies principally to NNS writers, whose
influence is mitigated in later periods. The nature of the interesting activity in these decades will
be considered in detail in section 4.7 beginning on page 128, which employs innovation
accounting as a statistical test of the polysemy of OTC.
4.5. BNC and BAWE corpus data
Comparing both yearly and decennial data from COHA to corpus data for native speakers
of English who were not native to the U.S. could yield interesting results, which could lead to a
number of interesting general observations. Two such non-U.S. corpora are the British National
Corpus (BNC) and the corpus of British Academic Written English (BAWE)
(Gardner & Nesi, 2012). First, a comparison of the COHA and BAWE data reveals some very
striking differences in usage. After removing instances of direct quotes from an outside source,
the documents written by self-reported native speakers of English contained 26 instances of
OTC. Of those, fully half were of the Contrastive variety, which slightly outnumbered instances
of the Corrective (Table 20):
Table 20: BAWE English speakers
Variety Count %Corrective 11 42.31%Contrastive 13 50.00%Alternative 2 7.69%
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Comparing the COHA and BNC data with for the decade of the 1980s, an incidence of overall
greater usage in the British corpus seems to emerge, at least initially:
Table 21: BNC and COHA per 10k, Corrective and Contrastive, 1980–1989
Corrective Contrastive
BNC 0.0961 0.0103
COHA 0.0549 0.0044
The Corrective data for the BNC corpus is 1.75 times as large of that of the COHA, while BNC’s
Contrastive data is 2.34 times larger than COHA’s. However, one similarity to COHA usage is
abundantly clear. Similar to the COHA results, Corrective usage in the BNC far outweighs usage
of the Contrastive, as shown in Figure 20. Due to the relatively short time range examined, and
the use of yearly data, bar charts are used instead of the line graphs used in earlier discussions:
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
0.180
0.200
BNC Corrective
BNC Contrastive
BNC Corrective 0.06270.06960.01930.10560.10900.03950.01580.14720.04680.19170.09340.09230.08370.08690.09710.08930.08540.1511
BNC Contrastive 0.00000.00000.00000.00000.00000.01970.01580.02010.00000.03290.00410.01360.00520.00550.00730.00300.00170.0000
1976 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
Figure 20: BNC Corrective & Contrastive per 10k, 1976–1994
Although the data shows some year-to-year variability, the Corrective data for the BNC data
generally hover around an average near 0.09 instances per 10,000 words in the corpus, while the
Contrastive data in the subset of the 1980s finds its average near 0.01 per ten thousand words.
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This finding might initially seem at odds with the downward slope in COHA data. However, that
assumption is not supported by the data, since the slope in COHA data reflects its extension into
a much longer time period than is covered by BNC. In the single complete decade for which
BNC data is available, COHA data is also fairly stable (Figure 21).
Looking next at the available yearly data, that initial perception seems to hold true for
Corrective data, but not for Contrastive. For the Corrective variety, tokens per 10,000 words in
the BNC corpus are greater than those in COHA for 13 of the 18 available periods, including the
entire span from 1986–1994 (Figure 21):
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
0.180
0.200
BNC Corrective
COHA Corrective
BNC Corrective 0.06270.06960.01930.10560.10900.03950.01580.14720.04680.19170.09340.09230.08370.08690.09710.08930.08540.1511
COHA Corrective 0.05070.08780.08460.05890.05170.04120.0797 0.06 0.05360.0528 0.061 0.03590.05320.04630.05830.02940.07480.0491
1976 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
Figure 21: BNC & COHA Corrective per 10k words, 1976–1994
Meanwhile, the comparing the Contrastive data of the two corpora over a reduced time span
offers at least tentative support for the perception that British usage is consistently higher
(Figure 22):
120
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
BNC Contrastive
COHA Contrastive
BNC Contrastive 0.0000 0.0000 0.0197 0.0158 0.0201 0.0000 0.0329 0.0041 0.0136 0.0052
COHA Contrastive 0.007853513 0 0.012356892 0.007965087 0.004002651 0 0 0.003590539 0.003984967 0.004095611
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
Figure 22: BNC & COHA Contrastive per 10k words, 1980–1989
In general, at least from the limited data for the full decade from 1980–1989, the BNC data tends
to be greater, often to a striking degree. This lends some evidence to the hypothesis that although
Contrastive OTC may have declined in British usage, its decline has not necessarily been as
marked there as it has been in the U.S.
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4.6. Diachronic relationship of OTC to in/by contrast
Characteristically, during the diachronic processes involved in language change, new
meanings and functions coexist with older ones for a relatively long period of time (Frank-Job,
2005, p. 412 n7). Within the COHA corpus data this economy seems to have been demonstrated
in U.S. English usage with Contrastive OTC. In earlier decades of the corpus, a diametric
opposition within a contrastive relationship between P1 and P2 that was primarily expressed
using the Contrastive version of OTC. The need to express that semantic schema of strong
contrast still exists, and speakers and especially writers still need a means of conveying it to their
respective audiences. So since the Contrastive variety has been declining in U.S. English since at
least the 1870s (if not earlier, see Figure 23) and stands at particularly low use in recent decades,
there must be another way to express the same semantic content. And there are indeed a number
of alternative lexicalized phrases readily available – perhaps most obviously, the phrases in
contrast, by contrast and on the other hand. Perhaps less obviously, the one-word particle
however could also function as a replacement for Contrastive OTC, particularly in a syntactic
construction that places the particle between commas rather than in clause-initial position.
Clause-initial however is perhaps better suited as a replacement for Corrective OTC.
Borkin (1979) treats in contrast and by contrast as perfect substitutes for each other, and
outlines four discourse functions of the in/by contrast pair:
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Typically, they link sections of text which describe characteristics of two entities that one
would have reason to expect to be similar, viewing these characteristics as not just different
but as polar or near-polar opposites.
Can be used when the same entity or members of the same class exhibit opposing
characteristics at different times.
Can also be used in juxtaposing two states of affairs involving completely different entities
Polar opposition... not in a highly conventionalized semantic contrast but rather in the way
an author views the situation he is presenting. [For example,] two courses of action…
presented as representing an either/or choice. (pp. 45–46)
These observations are in line with various prescriptive and descriptive texts about English usage
(for typical examples see Blanpain, 2008; Herbst et al., 2004; Schleppegrell & Colombi, 2002).
Borkin also provides an example of each of her four types, repeated below. One striking aspect
of these examples is that each of them also presents an ideal environment for Contrastive OTC to
be employed, as evidenced by examples within the COHA corpus. For each of Borkin’s
examples, an analogous example of Contrastive OTC, drawn from COHA data, is provided for
comparison. The discourse particles in contrast, by contrast and on the contrary are underlined
and presented in boldface below, for clarity. Each of Borkin’s four discourse uses is offered as a
separate bullet point item, followed by a matched pair of examples:
Two entities expected to be similar, but presented as polar or near-polar opposites (Borkin, 1979):
(42) ... normal human skin fibroblasts are capable of repair replication after UV
irradiation. In contrast, fibroblasts from patients with the rare hereditary skin
disease xeroderma pigmentosum exhibited essentially no repair replication. (p. 45.
Borkin’s example 1)
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(43) Notice that in a single-phase generator half the surface of the armature core has to
be left vacant. In a polyphase generator, on the contrary, the windings may cover
the entire surface, and usually do.
The same entity or members of the same class exhibit opposing characteristics at different
times (Borkin, 1979, p. 45):
(44) … it takes many trials to teach hungry rats to press a bar for a food reward, and
the rat never learns the trick if the food is not presented immediately after the bar is
pressed. In contrast a rat learns to avoid poisoned food in a single trial, in spite of
the fact that it may not get sick until six hours after it has eaten. (Borkin. 1979,
p. 46. Borkin’s example 2)
(45) In March, April, and May, according to the Wall Street Journal, the market value
of all listed stocks fell over fourteen billion dollars, while the short interest rose to
its May 25 peak. In June, on the contrary, while the short interest declined, the
market value of all stocks rose nearly five billion dollars.
Juxtaposing two states of affairs involving completely different entities (Borkin, 1979, p. 46):
(46) During the early years of prohibition ... alcohol consumption in Canada and the
U.S. fell to the lowest level [sic] for which there are data. In 1969, by contrast,
when general stores in Finland were allowed for the first time to sell beer, there was
an immediate increase in overall consumption. (Borkin, 1979, p. 46. Borkin’s
example 3)
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(47) For in Germany the innovations in administrative technique, in machine
production, and the rest, if not quite so far advanced as in France or in England,
nevertheless had been prepared by the gradual training of generations of former
medieval clerks and craftsmen in more and more technically-advanced ways – as
had been the case in England and France themselves; for fundamentally Germany
was part of the same general society as were England and France. In Egypt, on the
contrary, the same events tended to destroy what craftsmen's skill and what
intellectual soundness had in fact existed there in the eighteenth century.
Polar opposition... not in a highly conventionalized semantic contrast but rather in the way
an author views the situation he is presenting (Borkin, 1979, p. 46):
(48) Mr. Thorpe had the chance to stand up for a principle, and he chose not to take it.
Suppose, by contrast, that he had had the courage to meet the forces of prurience
head on and concede from the beginning his warm association with Mr. Norman
Scott, and asked the whole array of British citizens to examine in their conscience as
to whether there were not things in their own pasts that they could recall only with
embarrassment and regret. (Borkin, 1979, p. 46. Borkin’s example 4)
(49) Chile, Senora Rosa bragged, was the England of Latin America, which Amanda
speculated to herself meant they ate lots of greasy chips. She tried unsuccessfully to
imagine British people in what she imagined to be the Queen's English claiming to
be "Yes, quite the Chileans of Europe," and incredulous, she remembered two wars
fought in her own country so as not to be the England of North America. Her
mother, on the contrary, had given her the impression that Chile was Machu
Picchu, and that the Chileans were all Indians.
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From these examples it is clear that the semantic and syntactic environments common to
Contrastive OTC coincide precisely with the full range of environments that Borkin (1979)
described as being suitable for use with in/by contrast. This offers further evidence that the three
discourse particles serve precisely the same discourse functions and are pragmatically
indistinguishable (and thus interchangeable).
Tokens of in contrast and by contrast occur with increasing frequency over the relevant data
periods: they hover near zero over the first century or so, became pragmaticalized in roughly the
same period that Contrastive OTC entered the latter stages of its descent into disuse, and then
rocketed up as Contrastive OTC sank into near-zero usage (Figure 23):
Contrastive OTC, By contrast, In contrast
In contrast
OTC Contrastive
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
By + In contrastBy contrastIn contrastOTC Contrastive
By + In contrast 0.001 0 0 0 0.004 0.002 0.003 0.004 0.003 0.009 0.013 0.017 0.043 0.058 0.074 0.099 0.129 0.094
By contrast 0 0 0 0 0.003 0 0.001 0.002 0.003 0.006 0.007 0.007 0.017 0.035 0.046 0.051 0.061 0.048
In contrast 0.001 0 0 0 0.001 0.002 0.001 0.001 0 0.002 0.006 0.010 0.025 0.023 0.028 0.048 0.067 0.045
OTC Contrastive 0.115 0.080 0.077 0.079 0.072 0.041 0.036 0.038 0.037 0.019 0.026 0.013 0.016 0.011 0.005 0.003 0.003 0.001
1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 23: Contrastive OTC, clause-initial In/by contrast 1830s–2000s
The decennial evidence is suggestive: clause-initial in/by contrast “appears on the map” in the
1870s, at precisely the period that Contrastive OTC undergoes a very sudden and steep drop.
[The apparent drop in Contrastive OTC from the 1830s to the 1840s is instead the result of an
atypical spike in the former decade, and does not reflect a genuine trend in the data.] Both OTC
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and in/by contrast hold fairly steadily at their respective positions for four decades, then both
undergo sudden and relatively large changes in the 1920s. Once again, Contrastive OTC is
falling, while clause-initial in/by contrast is rising. The drop in in/by contrast from the 1990s to
the 2000s is quite likely an artifact of the composition of the COHA corpus data, which is very
incomplete for the final decade. In all, the evidence strongly suggests an account in which in/by
contrast are reanalyzed as discourse particles and then used with increasing frequency at the
expense of the older but Contrastive variety of an ambiguous OTC.
The first token of sentence-initial in contrast, which seems to mark its emergence as a
sentence-adverbial in the process of pragmaticalization (Lewis, 2011), appears in prose in the
COHA database in 1876, during the period of the steep drop in the rate of contrastive OTC use.
That example, from the Nonfiction genre, follows (50):
(50) Speaking of barns reminds me that I do not remember to have seen a building of
this kind while in England, much less a group or cluster of them as at home; hay and
grain being always stacked, and the mildness of the climate rendering a protection
of this kind unnecessary for the cattle and sheep. In contrast, America may be
called the country of barns and outbuildings.
At least in U.S. English,16 historical COHA corpus data for OTC offers persuasive evidence of
semantic narrowing through time. On this view, a cultural preference resulted in selection of the
Corrective sense over the overlapping Contrastive senses in a network of usage. This in turn
resulted in the Contrastive strand being all but pruned away, while a strongly preferred
Corrective usage remains well-attested in use to this day. Although the move away from
Contrastive OTC in the U.S. is cointegrated with a decline in use of Corrective OTC (and thus of
16 The term “North American English” is deliberately avoided here because U.S. usage is distinguished as much as possible from all others (including Canadian – Canada, Mexico and other smaller sovereign states are also in North America) for a greater degree of parsimony.
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OTC use overall), those trends coincided with a complementary increase in the use of related
terms (in contrast, by contrast) that serve the same discourse purpose that OTC was shedding,
offering further evidence for semantic narrowing. The latter two particles were reanalyzed from
prepositional phrases into sentence adverbials with a discourse particle function. Similar written
data from synchronic British English corpora suggests, however, that British English writers did
not participate in the narrowing process for OTC to the same degree.
4.7. Innovation accounting as a test of polysemy of OTC: 1850–1910
The rationale for use of FEVDs is discussed in section 4.1. As discussed at length in
earlier sections, the interaction between discourse particles within a corpus is a composite of
numerous individual stylistic choices and decisions among authors. These choices do not follow
a deterministic path, so it is not possible to extract from the data any clear or concrete guide to
writers’ decision making processes. Since this system is not deterministic, even if one could
control the use of any given particle within a given genre or population of writers/speakers, any
resulting change in the usage of related particles (or other linguistic forms, such as prepositional
phrases) would not be strictly predictable – neither the degree nor even the direction of such
change could be reliably predicted within the usual confidence levels. This in turn is an outcome
of the Firthian principle of linguistic polysytemicism (Crystal, 2011):
…language patterns cannot be accounted for in terms of a single system of analytic
principles and categories (monosystemic linguistics)... but that different systems may
need to be set up at different places within a given level of description. (Entry under
“Firthian”)
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As but one example, different systems may exist within different literary genres, reflecting in
part their disparate rhetorical goals as well as conventions regarding language use (e.g., a greater
or lesser degree of informality). However, saying that meaning and context do not interact
mechanically is very far from saying that they do not interact in an informative manner. In
general, the difference in semantic content of Corrective versus Contrastive OTC should not only
have a clear impact on their syntactic form, but also on their interaction with other discourse
particles in a diachronic corpus over a given time horizon. A set of hypotheses could thus be
posited which are specific to the framework of polysemy between Corrective versus Contrastive
OTC, and also to the theoretical framework involving the dynamics of social forces acting on a
discourse group. Adopting an approach that matches the semantic properties of a word with its
interactions with other lexical items would be in line with the assertion by Cruse (1986) that “the
semantic properties of a lexical item are fully reflected in the appropriate aspects of the relations
it contracts with actual and potential contexts” (p. 1). These hypotheses also reflect yet another
Firthian principle in linguistics summarized in the dictum “You shall know a word by the
company it keeps” (Firth, 1957, p. 11). However, given the nature of the linguistic domain at
hand, statistical methods such as Ordinary Least Squares (OLS) may not be appropriate for the
task. A new approach is thus desirable: forecast error variance decompositions (FEVD, also
known as innovation accounting) derived as an ancillary to vector autoregression modeling. The
polysytemicism inherent within the domain of linguistics presents a suitable workspace for
examining the Granger-causality in the relationships between data, which is captured in VAR
models. As Woodward (1995) states, “[Granger-causation] is… an information-relative notion:
one variable may [Granger-cause] another relative to a certain information set but not relative to
some other” (p. 18). One key contribution of this present analysis is the assertion that FEVDs as
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an ancillary of vector autoregression analysis can be imported fruitfully into Applied Linguistics
research that utilizes corpus data. In particular, FEVDs can be used to generate a new form of
Behavioral Profile approach to lexical semantics (Gries, 2010b; Hanks, 1996) based on vectors
of Granger-causality between lexical items (in this case, discourse particles). Other concepts and
methods of VAR analysis, such as impulse response functions (IRFs) and historical
decomposition, may be employed in future research.
To examine these interactions we invoke a commonsense distinction between particles
based on the breadth of the semantic fields they encompass. A loose hierarchy suggested by Bell
(1998) ranks discourse markers [Bell’s terminology] by their relatively “broader” versus
“narrower” set of semantic features, syntactic privileges and rates of occurrence (p. 531). In
Bell’s hierarchy, more specific particles are treated as residing within categories that are in turn
subcategories of broader categories. From narrowest to broadest, Bell’s categories are:
peripheral, secondary core, primary core, and nucleus. The particles selected for analysis in
conjunction with OTC were in contrast, on the other hand, sentence-initial however, and
sentence-initial in fact. However and on the other hand were selected for their broader semantic
range. For example, Quirk et al (1985, pp. 630–632) categorize OTOH in both the replacive and
antithetic subcategories of contrastive markers, while OTC and in/by contrast are restricted to the
category of the antithetic conjuncts. In the Halliday and Hasan taxonomy, both however and on
the other hand are categorized within both the contrastive and adversative subcategories of
adversative markers, suggesting a generality in their use and a degree of overlap in their
rhetorical goals. Both OTC and in/by contrast are only adversative. In this analysis, the semantic
import of in contrast is assumed to be very analogous to that of Contrastive OTC but not strictly
comparable to the Corrective variety (see extensive comparison in section 4.6, beginning on page
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122).
Twenty four vector autoregression systems (VARs) were generated and applied to time
series data in order to obtain the ancillary forecast error variance decomposition estimates. The
results of VAR analysis are sensitive to the Cholesky ordering of the endogenous variables
within the model. However and on the other hand, which are more general and flexible in their
rhetorical goals and semantic content, were presumed to be less sensitive to changes in OTC, and
thus were placed higher in the Cholesky ordering. Repeated sampling has shown that reversing
the order of in contrast and in fact results in no significant difference in the results. The sample
size used in every case was 15 years. This length of time was selected for statistical rather than
theoretical reasons. The VAR models at hand could in no case be estimated with fewer than 10
years of data, setting an absolute minimum at that boundary. Even using 10-year periods
occasionally caused estimation of the system to fail after an error state caused by a non-positive
definite matrix, presumably due to sampling variation issues associated with small sample size
(Rigdon, 1997). Interested readers may refer to Rigdon (1997) or West, Welck and Galecki
(2006, pp. 31ff.) for a technical explanation of this error state. However, extending the data range
to 15 years uniformly resolved the error condition. The models also employ a lag order of 1 in
every case, for two reasons: first, because there simply is no underlying theoretical motivation
for a longer lag, since it should not take any more than one year for key information to affect the
rhetorical decisions of authors. Second, the gretl software’s VAR lag selection function, which
computes as selection criteria Akaike’s (AIC), Schwarz's Bayesian (BIC) and Hannan-Quinn
(HQC) criteria, routinely suggested that specific lag order. Finally, although many of the
illustrative graphs in the previous sections of this analysis are drawn from decennial data, annual
data was employed for many reasons, including the larger sample size and greater informativity
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associated with yearly data, and the greater risk of problems (particularly autocorrelation)
inherent in the aggregation of temporal data (Christiano & Eichenbaum, 1987).
With these reservations in mind, a set of hypotheses can be posited and their impact on
the data probed using statistical approaches that are innovative in the field of linguistics. The
data can be examined for tendencies that reflect the semantic core of a given discourse particle,
and these tendencies can be incorporated within a larger theory. Within this framework, it seems
reasonable to first posit two general principles, and from these principles derive readily testable
hypotheses:
General principle 1: Other things being equal, and within a given genre, both Corrective
and Contrastive OTC should have a relatively richer degree of interaction with particles
that are rather higher on a hierarchy similar to Bell’s (that is, the more general particles,
with a broader semantic range and greater incidence) than they would with ones that are
lower (more specific).
General principle 2: Other things being equal, and within a given genre, particles should
display meaningfully richer interactive dynamics with particles of a similar semantic
content. That is, Contrastive OTC should interact more richly with contrastive particles,
and Corrective OTC should interact more richly with corrective ones.
The interaction between these two general principles could be further expanded into five testable
hypotheses, as follows. First, since the two variants of OTC reflect different rhetorical goals and
conceptual content, they should have an affinity for particles with similar semantic content. The
first two hypotheses are driven entirely by the relative degree of correspondence between the
semantic content of the particles, and represent the extreme ends of a preference for semantic
match and a corresponding rejection of semantic mismatch. They do not take into account any
relative preference for more general versus more specific particles:
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Hypothesis 1: Other things being equal, and within a given genre, the particle that
displays the richest interaction with Corrective or Contrastive OTC should be a particle
which agrees with the semantic content of the OTC particle. Corrective OTC should
interact most with either however or in fact, while Contrastive OTC should interact most
with on the other hand or in contrast.
Hypothesis 2: Other things being equal, and within a given genre, the particle that
displays the lowest degree of interaction with Corrective or Contrastive OTC should be a
particle which disagrees with the semantic content of the OTC particle. Corrective OTC
should interact least with either on the other hand or in contrast, while Contrastive OTC
should interact least with however or in fact.
The next two hypotheses compare like to like (general to general and specific to specific) under
the general rule that relative semantic agreement should be preferred over relative semantic
mismatch. However, bear in mind that there is some degree of semantic overlap between the
more general particles, since however can be used in contrastive and corrective schemas.
Therefore, the third hypothesis may well hold true more consistently than the fourth, and the
fourth does relatively less to confirm the polysemy of OTC:
Hypothesis 3: Other things being equal, and within a given genre, Corrective OTC should
have a richer degree of interaction with sentence-initial in fact than with in contrast,
while Contrastive OTC should display the opposite tendency.
Hypothesis 4: Other things being equal, and within a given genre, Corrective OTC should
have a richer degree of interaction with sentence-initial however than with on the other
hand, while Contrastive OTC should display the opposite tendency.
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The rank ordering logic becomes considerably less cut and dried when considering the
interaction between a discourse group’s characteristic preference for general particles over more
specific ones or vice versa, and the interaction of that preference with a preference for semantic
match. This final hypothesis actually describes two scenarios, based on a relative preference for a
semantic match:
Hypothesis 5: Within a given genre, in the absence of a strong preference for semantic
match, it is expected that the degree of interaction of Contrastive OTC with the four other
particles would be ranked as follows: on the other hand, however, in contrast and finally
in fact. For Corrective OTC, the expected ranking by interaction is however, on the other
hand, in fact, and in contrast. If preserving a semantic match is the overriding concern,
then the first two rankings should reflect a match, and the final two should not.
This hypothesis follows from straightforward assumptions. The first assumption is that because
the more general particles are flexible and can legitimately be used in either a contrastive or
corrective environment, they would become the default “all-purpose tools” in the average
writer’s lexicon. Second, the more specific particle which at the same time is less matched
semantically with the schema inherent in Contrastive or Corrective OTC should be the one least
frequently considered.
The results of the VAR analysis for two sets of variables (showing the interaction of
Contrastive OTC and Corrective OTC with the same set of variables) across three different
genres (Fiction, Nonfiction and Magazine) and four time periods resulted in the generation of 24
forecast error variance decomposition (FEVD) models. All of these models are presented in
chronological order in four Appendices to this text – one for each of the four time periods. The
first of these is Appendix G: Forecast error decompositions of variance (1850–1864), beginning
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on page 207, and the fourth is Appendix J: Forecast error decompositions of variance (1895–
1909), beginning on page 225.
In order to make the subsequent discussions clearer to the reader, the results of one
typical FEVD model are presented below, with an illustrative figure as an aid to understanding.
This fairly straightforward example details the results of the FEVD derived from the VAR model
for Contrastive OTC in the Nonfiction genre, for the period from 1895 through 1909. The data is
found on Table 62, on page 229 in one of the Appendices; however, to facilitate discussion, that
table has been duplicated below (Table 22):
Table 22: Decomposition of variance for Nonfiction Contrastive OTC (1895-1909)
period std. error NFCont NFhwvr_init NFotoh NFInc NFFac
1 0.0424595 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0507808 75.8338 3.7013 13.2559 1.5221 5.6870
3 0.0557743 63.0661 6.8164 12.0795 11.4657 6.5724
4 0.0569007 60.9562 6.8596 13.8078 11.2791 7.0973
5 0.0578043 59.1693 7.6716 13.6179 12.5662 6.9751
6 0.0579947 58.8313 7.7181 13.8561 12.5699 7.0246
7 0.0580948 58.6419 7.8062 13.8322 12.7084 7.0113
8 0.0581186 58.5995 7.8133 13.8618 12.7090 7.0164
9 0.0581305 58.5771 7.8234 13.8589 12.7255 7.0150
10 0.0581334 58.5720 7.8242 13.8626 12.7256 7.0156
A legend that provides an explanation of the abbreviations is presented in Table 23, below. For
example, the third column of the table above, labeled NFCont, provides values for the interaction
of Contrastive OTC with itself in the Nonfiction genre across the ten relevant time horizons.
Similarly, the column labeled NFhwvr_init displays the interaction of Contrastive OTC with
sentence-initial however within the same genre and across the same time horizons. The
abbreviations in Table 23 are used throughout this discussion:
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Table 23: Legend of abbreviations in tables in FEVD analysis
FIC: FictionNF: NonfictionMAG: MagazineCont: Contrastive OTCCorr: Corrective OTChwvr_init: Sentence-initial howeverotoh: on the other handINC: in contrastFAC: in fact
Returning to Table 22, the variable labels in the heading denote the discourse particles included
in the model, as follows: NF denotes Nonfiction, NFCont denotes the discourse particle
Contrastive OTC in the Nonfiction genre, NFhwvr is sentence-initial however, NFotoh is on the
other hand, NFInc is in contrast and NFFac is in fact. For illustration, the numeric results in this
table are depicted graphically in Figure 24 (below):
Figure 24: FEVD, Nonfiction (Contrastive OTC) 1895-1909, lag order 1
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As the legend indicates, Contrastive OTC is indicated by red shaded areas, sentence-initial
however by blue, on the other hand by green, in contrast by dark pink, and in fact by light gray.
The forecast error variance decompositions suggest a rich interaction among the
variables, initially in the second forecast horizon, and even more so in later ones. The nature of
these interactions in this particular case is well in line with the hypotheses of this analysis, but
conclusions about those hypotheses cannot be drawn from a single model. However, interesting
phenomena can be observed in this period. Since NFCont (Contrastive OTC in the Nonfiction
genre) is highest in the unidirectional Cholesky ordering of the variables, in the initial period
none of the lags of the other variables are included in estimation of NFCont’s forecast error
variance in that period. Therefore, NFCont explains all of its own one-step ahead forecast error
variance in that period, simply as a mechanical artifact of the structure of recursive VAR models.
This is indicated in Table 22 by the fact that the value of NFCont (as it interacts with NFCont,
that is, with itself) is 100.00% in the row indicating period 1. It is also illustrated graphically by
the solid vertical red bar in the left side (that is the first period) in Figure 24 (above). A notable
change in the contribution of in contrast to NFCont’s FEVD takes place between periods two
and three. Between the two- and three-step ahead FEVDs, the contribution of in contrast jumps
from 1.52% to 11.47%. This can be seen by examining the rows for periods 2 and 3 in the
“NFInc” column of Table 22, and also by the sharp increase in the size of the dark pink shaded
areas in the second and third columns of Figure 24. So the second vertical bar in the figure
depicts results that can be seen in detail by reading horizontally across the row labeled “period 2”
in Table 22: the large red shaded area represents 75.38% provided by NFCont itself, the green
shaded area (the second largest one in that column) is 13.26% contributed by on the other hand,
and so on.
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In the longest forecast horizon (ten steps ahead), NFCont explains 58.57% of its own
forecasts error variance, which is another way of saying that all of the other four variables
collectively explain 41.43%. The more specific particles in contrast and in fact, interact with
Contrastive OTC to a relatively strong greater degree, accounting for 12.73% and 7.02% of the
total explanation of the variance in the forecast error of NFCont, respectively. Note that the more
contrastive particle displays a richer degree of interaction than the more corrective one.
Similarly, among the more general particles, the more contrastive on the other hand, interacts
more with Contrastive OTC than does the relatively more corrective particle however, at 13.86%
and 7.82% respectively. Overall, then, the results from the FEVD for Nonfiction Contrastive
OTC in the period from 1895 through 1909 concur with the hypotheses of this analysis.
That example illustrates the most salient points of the summary data in this section. The
analysis was carried out as follows: as an initial step, four summary tables were compiled of the
10-step-ahead data from all the forecast error variance decompositions located in the
Appendices. The first table (Table 24, on page 140) summarizes data regarding OTC interacting
with its own current and lagged values. It is located in this section (rather than in an appendix)
and considered separately, for two reasons: first, individual lines will be discussed, both as a
component of the analysis and in comparison with lines from with Table 22 (page 135). Second,
the goals pursued while examining this table are different than those that will be pursued using
the other tables. The first discussion deals with overall levels of activity as well as shocks to the
system. Later discussions will consider genre-driven interactions of lagged and current values of
the two varieties of OTC with those of four other discourse particles (in fact, in contrast,
however, and on the other hand) within three genres. Those three tables, located in an Appendix
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beginning on page 231, contain data for the Fiction (Table 64, page 231), Magazine (Table 65,
page 232), and Nonfiction (Table 66, page 233) genres.
The first table depicts the reactions of the relevant OTC particle (that is, Contrastive OTC
or Corrective OTC within a given genre and time period) with itself in the 10-step-ahead
horizon. So for example, the first line is the 10th horizon data for NFCorr (that is, Corrective
OTC in the Nonfiction genre) in the FEVD of NFCorr in the period from 1880 through 1894,
from line 10 of Table 57: Decomposition of variance for NFCorr (1880-1894) on page 224. The
second line is the 10th horizon data for NFCont in the FEVD of NFCont in the period from 1865
through 1879, and so on. In general, it is useful to bear in mind that the higher values on the
table reflect a lower degree of interaction between OTC and other particles, since they represent
a higher degree of interaction between OTC and itself. Lower values thus reflect a greater degree
of interaction between the different discourse particles. By logical extension, this table also
displays indirectly the overall degree to which the other four particles do or do not interact with
Contrastive OTC or Corrective OTC (Table 24):
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Table 24: Summary of FEVDs, 10th horizon, OTC with itself
Genre/OTC Date range interaction
1. NFCorr 1880-1894 94.2852
2. NFCont 1865-1879 92.6603
3. FICCont 1880-1894 92.6289
4. FICCont 1851-1864 91.4847
5. FICCont 1895-1909 91.1641
6. FICCont 1865-1879 88.5328
7. MAGCont 1865-1879 86.8001
8. MAGCorr 1895-1909 84.3239
9. NFCont 1880-1894 82.9831
10. MAGCorr 1865-1879 80.6757
11. FICCorr 1865-1879 79.46
12. FICCorr 1895-1909 79.4032
13. MAGCorr 1880-1894 78.0337
14. MAGCont 1895-1909 77.2002
15. NFCont 1851-1864 74.8502
16. MAGCorr 1851-1864 68.3434
17. NFCorr 1851-1864 67.4111
18. NFCorr 1865-1879 66.0255
19. MAGCont 1851-1864 59.5853
20. NFCont 1895-1909 58.572
21. NFCorr 1895-1909 58.1155
22. FICCorr 1851-1864 55.6756
23. MAGCont 1880-1894 52.6142
24. FICCorr 1880-1894 50.1087
The summary table above carries over data from the tenth row (the tenth horizon, in this case) of
every FEVD table in the Appendices. To relate this table to the previous example, compare the
value of NFCont (Contrastive OTC for the Nonfiction genre) in the last column of row 20 to the
value in the tenth row of Table 22 (page 135). The values are identical (i.e., both are 58.572).
Moreover, since Table 24 above is sorted and ranked by the last column, the value for NFCont in
the 10th forecast horizon of 1895-1909 was only the 20th out of all 24 VAR models. This
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relatively low degree of interaction of current and lagged values of NFCont with its own forecast
error variation disturbances can also be interpreted to mean that other variables interacted
strongly with NFCont in that forecast horizon (and indeed in all later horizons).
The values in this table bear close observation, since anomalous results can indicate
either the presence of exogenous shocks to the system, or indicate a system in which the test
variables has little or no overall interaction with other endogenous variables in the VAR model.
If the percentage of Granger-causation between a variable and itself is high, as for example for
the top-ranked Nonfiction Corrective OTC (NFCorr) in 1880–1894, then other variables do not
exert much influence during that period. As Sims (1982) states:
A natural measure of the degree to which Granger causal priority holds is the percentage
of forecast error variance accounted for by a variable’s own future disturbances in a
multivariate linear autoregressive model... A variable that is optimally forecast from its
own lagged values will have all its forecast error variance accounted for by its own
disturbances. (pp. 131–132)
Recall that VAR models such as these are constructed with the assumption that all variables are
endogenous – that is, any influence on the variables in the model can only come from its own
current and lagged values and the current and lagged values of the other variables within the
model. That is not quite the same as saying that absolutely no other element in the relevant
domain has any influence on these variables; instead, it is saying that the combined influence of
all other possible variables is assumed to be negligible. In a VAR system, then, if there is little or
no interaction between one variable and others within the model, there can only be two possible
explanations. The first explanation, quite simply, is that there genuinely was little or no
interaction between the given variables. In that case, the other values display little or no Granger-
causality upon the test variable – the interaction between them is weak or nonexistent. The
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second possible cause for the case in which the percentage of forecast error variance reveals little
interaction between OTC and all others in a system could be that is that there is in fact an
exogenous variable at work – a variable which is not included within the VAR model. The
assumption that all variables are endogenous within a VAR model breaks down if an exogenous
variable really does exist and exerts a significant influence.
Looking at Table 24, one key observation is that four of the top six entries are for
Contrastive OTC in the Fiction genre. The smallest degree of interaction among these is found in
the period from 1880 through 1894, which reveals remarkably little interaction between
Contrastive OTC and the other four particles. This data is presented graphically in Figure 25:
Figure 25: FEVD, Fiction (Contrastive OTC) 1880-1894, lag order 1
The numeric results which this graph depicts are presented in detail in Table 52: Decomposition
of variance for FICCont (1880 – 1894) on page 219. In the figure above, the low overall degree
of interaction between Contrastive OTC and the four other variables is evident in the size
difference between the large area that is shaded red, which represents the interaction of
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Contrastive OTC with itself and its own lagged values, versus the quite small size of the shaded
areas of every other color, representing the interaction of OTC with the four other variables.
Results for the other three 15-year periods show comparably low levels of interaction among the
variables. Details for the other three periods are presented in the following tables: Table 40 on
page 207, Table 46 on page 213, and Table 58 on page 225. The low interaction between
Contrastive OTC and other particles in the Fiction genre confirms an earlier discussion about the
mismatch between the semantic content of Contrastive OTC and the rhetorical goals that are
typical within the Fiction genre.
Recall that there is a significant difference in usage rates between Corrective and
Contrastive OTC in the Fiction genre (and indeed across genres). This was previously illustrated
in Figure 12 on page 96. For convenience of exposition, that graph is duplicated below. Both
time series lines are particularly stable, so there is very little volatility in either time series
(Figure 26):
CORR FIC
CONT FIC
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CORR FICCONT FIC
CORR FIC 0.114 0.172 0.158 0.159 0.165 0.181 0.139 0.157 0.120 0.103 0.106 0.084 0.085 0.074 0.060 0.039 0.022 0.030 0.030
CONT FIC 0.029 0.039 0.058 0.031 0.034 0.031 0.023 0.016 0.020 0.019 0.007 0.016 0.005 0.005 0.001 0.005 0.000 0.001 0.000
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 26: Fiction genre, Corrective versus Contrastive OTC, 1820s-2000s
The results for Contrastive OTC within the Fiction genre were uniform across 60 years of data:
there was remarkably little interaction between Contrastive OTC and the other four discourse
particles treated as variables. For example, from row 10 of Table 40: Decomposition of variance
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for FICCont (1851-1864) on page 207 (relevant tables are presented in the Appendices) changes
in Contrastive OTC account for 91.48% of the variance of its own forecast error, which in turn
means that all other endogenous variables contribute only 8.52% to the total impact to OTC. The
8.52% is broken down into more specific percentages. Again reading across the 10-year forecast
horizon, changes in however accounts for only 2.36% of the forecast error, on the other hand
contributes a particularly small influence at only 0.79%, in contrast 3.47%, and in fact
contributes 1.90%. In all, there is simply very little interaction between these variable and
Contrastive OTC within the Fiction genre in the given time horizon. These results are consistent
across three other 15-year periods as well. This finding is perhaps unsurprising, given the
relatively low usage in that genre of Contrastive OTC across all time periods, as depicted in
Figure 26 (above). That would in turn imply a lower rate of interaction within this genre between
Contrastive OTC and other particles.
The conclusion drawn from this is equally straightforward. The uniformly low-intensity
interactions between Contrastive OTC and all other variables across all four periods (a total of 60
years) reflect the previously-discussed supposition that its semantic content is not strongly
concurrent with the rhetorical demands placed upon writers within the Fiction genre. The
rhetorical demands of the genre do not generate a consistent need for the use of Contrastive
OTC, and do not moreover require a significant amount of interaction between that and other
particles within any given text. Thus there was simply little motivation for authors in the Fiction
genre to employ Contrastive OTC, to choose between using it as opposed to other particles, or to
share in the social trends that presumably motivated structural changes in other genres. In short,
Contrastive OTC within the Fiction genre presents an atypical case. This finding will bear on
conclusions drawn in subsequent discussions.
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Finally we turn to the key question: how can the FEVD models help probe the polysemy
of OTC? A sequence of steps is required to get at the relationship between Corrective/Contrastive
OTC and variables assumed to be relatively contrastive (here in contrast and on the other hand)
or relatively corrective (here in fact and however). In general, it is difficult to draw strong
conclusions from the data at this stage of the analysis, since comparing values across genres
means comparing them across discourse groups, each of which has its own system of rhetorical
goals and stylistic preferences. Writers selecting discourse particles for a written text do so
within a system of expectations (often not directly spoken) that are a characteristic of the genre.
That is, the expression of the complex sets of social goals of different discourse groups –
expressed here as texts within separate genres – should drive the resulting patterns of usage. The
first step then is to sum the forecast error variance decomposition values within each genre,
which results in three tables in Appendix K: Summary tables of FEVDS, 10th horizon, beginning
on page 231. These in turn need to be divided into Corrective OTC versus Contrastive OTC
groups. Recall that Corrective schemas can only rarely be recast into Contrastive ones, since the
two semantic goals – correcting one idea about a single referent versus comparing two separate
referents – are not interchangeable. The two main varieties are separate and distinct, and involve
different selection processes to choose an appropriate discourse particle (if any). Dividing the
results for the three genres along the lines of semantic schema then results in six more
intermediate tables, given in Appendix L: Ranked normalized particle interactions, by genre on
page 234. The rows in these tables are ranked in descending order, by Z-value. Note that
negative Z-values do not indicate any form of “negative interaction”, but instead designate a
level of interaction that is below average within the relevant population, as a function of the
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nature of Z-values. By the same logic, negative values closer to zero indicate a relatively richer
level of interaction than those farther from zero.
The results of these tables can be combined into one Behavioral Profile (Gries, 2010b;
Hanks, 1996) composed of vectors of Granger-causality, constituting a new approach to these
issues. In usage-based cognitive-linguistic research within a paradigm of lexical semantics, a
“profile” refers to a general pattern of systematic relationships between a word and other
sentential constituents based on the semantic roles of the words (Gries & Divjak, 2009, p. 60).
The method of tying Behavioral Profiles to vector autoregression analysis (via FEVDs, or
innovation accounting) as a test of polysemy of a word constitutes an original contribution of this
current analysis to the literature on polysemy.
In Table 25 (below), the first four columns of data are Z-values describing the degree of
interaction between the particle and the relevant variety of OTC within a given genre, as
determined from the VECD analyses. The values are normalized for ease of comparison.
Numbers in parentheses indicate the rank ordering of the results across rows, by Z-value in
descending order. The value of 1.42 in the top left corner, then, refers to the interaction of the
particle however with the particle Contrastive OTC as occurs within the Fiction genre. The
number 1 in parentheses indicates that out of the four variables tested, the particle however has
the greatest degree of interaction with Contrastive OTC in the Fiction genre. In the columns
numbered one through five, results are labeled Y or N based on whether they confirm or refute
the five hypotheses described earlier (Table 25):
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Table 25: Behavioral profile of particles in genre/OTC variety combinations
Genre/OTC however otoh in contrast in fact 1 2 3 4 5
FICCont 1.42 (1)
-0.89 (4)
-0.14 (2)
0.40 (3) N N Y N N
FICCorr -0.51 (3)
1.48 (1)
-0.27 (2)
-0.70 (4) N N N N N
MAGCont 0.11 (2)
1.36 (1)
-0.53 (3)
-0.93 (4) Y Y Y Y Y
MAGCorr -0.58 (3)
-1.05 (4)
0.49 (2)
1.15 (1) Y Y Y Y N
NFCont 0.23 (2)
1.22 (1)
-0.26 (3)
-1.19 (4) Y Y Y Y Y
NFCorr 0.75 (1)
-1.46 (4)
0.17 (3)
0.53 (2) Y Y Y Y Y
Using this Behavioral Profile, testing the hypotheses laid out in the opening of this section
becomes a straightforward exercise. The table indicates that the results vary, but they vary in
conspicuously consistent ways that may offer meaningful refinements on the stated set of
expectations.
One pattern is immediately obvious: the Fiction genre stands wholly and completely apart
from the Magazine and Nonfiction genres. Genre-based disparities in the results should be
anticipated as an expression of the polysytemicism inherent linguistic behavior, and reflected in
genre-driven corpus analysis. A lack of such disparity might even tend to cast doubt on the
reliability of the findings. By extension, this confirmation of polysytemicism can be taken as
evidence in favor of the reliability of the FEVD analysis technique proposed here.
In the Fiction genre, the hypotheses were rejected nine out of ten times. The finding that
Contrastive OTC performed marginally better with respect to the five hypotheses than Corrective
was solely due to the ranking of in contrast over in fact. Taken at face value the results seem
quite counter-intuitive. Rather than constituting a rejection of the hypotheses of this research,
however, the reliability of that face value should be discounted, and the specific details of the
results for this genre should be understood to offer only very minimal insight. Instead, on the
whole these results confirm once again a recurrent theme throughout this analysis: the rhetorical
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goals inherent within the Fiction genre are not deeply consonant with the practice of structuring
logical arguments, and so that genre does not apparently present fertile ground for testing
hypotheses about the polysemy of OTC. It is probably also significant that texts in the Fiction
genre tend to adopt an informal tone, while use of OTC is clearly more characteristic of a formal
register.
Setting aside the Fiction genre, there are twenty remaining opportunities to confirm the
hypotheses of this analysis within the Magazine and Nonfiction genres. Hypotheses 1 and 2,
which reflect the strongest ends of a preference for semantic match and a rejection of semantic
mismatch, are confirmed in every remaining case. Those two hypotheses speak very directly to
the question of semantic content and its impact upon the relationships between words, which is
the key issue in this investigation of polysemy.
In the middle portion of the rankings for each row – positions 2 and 3 – there are
numerous possible rank orderings of preference. It seems that at least in the Nonfiction genre,
corrective schemas activate a preference for selecting a semantic match, whereas within a
contrastive schema the preference is for “general versus specific,” while still preserving a
relative preference for semantic accord. When the rhetorical goal is one of correction, a premium
is placed on the accuracy and precision of the arguments laid out in a text, and so writers may
devote particular attention and precision to the task of ensuring that a correct concept should not
be held on an equal footing with an incorrect one. In this case, the more semantically aligned
discourse particles resonate more richly with that goal. When the semantic schema is one of
comparison, the need for rigor is somewhat relaxed, and the tendency to default to more general-
purpose discourse particles finds more frequent application. These findings offer a perfectly
reasonable account of the expected behavior of writers in the Nonfiction genre, which is
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distinctive in that it relies heavily on the careful parsing of contrasts and comparisons (Bell,
2004, p. 2179).
In the Magazine genre, the interactions of the four tested particles with Contrastive OTC
again constitute a strong and consistent argument in favor of polysemy. The ranked results
duplicate the pattern of those in the Nonfiction genre, favoring “general over specific,” within an
approach systematically preserving semantic consonance. Within both the Magazine and
Nonfiction genres, these patterns of interaction implicitly reflect “the way things are done.” With
respect to Corrective OTC, the strongest interaction is again with a semantically similar particle
and the weakest with one relatively dissimilar. Once again this displays the dynamics of selecting
words based on semantic content, and offers strong support for a dual-meaning reading of on the
contrary.
Out of all 24 of rankings of the four particles in the Behavioral Profile described in Table
25, only one is genuinely counter-intuitive to any meaningful degree: the second-place ranking
of in contrast with respect to Corrective OTC in the Magazine genre. According to this analysis,
in a schema strongly preferring a semantic match, in contrast should have been third or fourth,
and in one favoring “general over specific” while preserving a preference for semantic match, in
contrast should have been dead last. It seems probable that this single inconsistency, while
perhaps bearing further investigation, reflects a statistical anomaly in the data. The weight of
evidence reflected in the Behavioral profile in Table 25 suggests that this finding does not disturb
the arguments in favor of polysemy.
The confirmation of these hypotheses in nineteen of twenty cases presents resounding
support for the underlying assertion that OTC has two separate meanings. The Corrective and
Contrastive variants reflect different semantic goals that coincide perfectly with the goals of
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other contrastive or corrective particles (such as in fact and in contrast), and their relative degree
of interaction with such particles indicates that writers take into account the whole set of
meanings or senses that each particle possesses. If Contrastive OTC and Corrective OTC were
not polysemous, then why should they pattern so predictably with such dissimilar particles? To
the same degree that Firth’s (1957) assertion that words’ senses are indicated by their interaction
with other words holds true, as well as the assertion by Cruse (1986) that “the semantic
properties of a lexical item are fully reflected in the appropriate aspects of the relations it
contracts with actual and potential contexts” (p. 1), both Contrastive and Corrective OTC are
expressing their respective identities vigorously in their interactions with other discourse
particles, as demonstrated in their Behavioral Profiles drawn from vectors of Granger-causality.
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5. Pedagogical approach
The three goals of this analysis are to assert the polysemy of on the contrary and establish
it with corpus-based statistical findings, to embed the analysis within a solid theoretical
framework, and to offer pedagogical aid. The polysemy of the discourse particle is largely an
outcome of its semantic makeup, and has important, characteristic syntactic consequences. The
theoretical underpinnings are based largely on consideration of cognitive processes. Pedagogy is
more concerned with usage rates (historical and present, with an emphasis on the latter),
syntactic forms, and communicative goals.
The main goal of this pedagogical section is to help teachers refine their explanation of
on the contrary to reflect actual usage. EFL/ESL textbooks and other reference sources offer an
over-simplified explanation that may sometimes do more harm than good. In contrast, many
finely-detailed points are presented in this section. Some of these points may be more suitable for
advanced learners. However, even at an intermediate level, students should have a clear
understanding of at least the most common discourse use of OTC and at least two or three of the
most common syntactic forms. If educators use the details of this account to deepen and refine
their understanding of the more finely-grained details of the nature of OTC, they can then decide
for themselves which elements or aspects are the most suitable for their students’ needs. Some of
the more general points that teachers may wish to raise their students’ awareness of include the
following:
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OTC has two meanings, although one is rare
The most common and therefore “safest” meaning is used when correcting an idea
that is untrue or incorrect
The location where OTC is placed in a sentence (the syntax, or “grammar”) is
very important. Each location has a very strong correlation with one or the other
meaning, so putting it “in the wrong place” can seem to change its meaning, and
make it seem strange or wrong.
The nationality of the reader may also be important. The rare form of OTC is
more acceptable in U.K. usage than in the U.S.
Teachers may also decide to avoid mechanically marking OTC as “wrong” when it is in fact used
correctly, albeit in a rare form.
Because this section focuses on ways to present the technical information within this
analysis to students in the classroom, it is to some degree a non-technical summary of several
other sections of this analysis. Photocopiable images that are highly suitable for classroom
explication are presented separately (see Figure 1, page 37; Figure 2, page 38 and Figure 27,
page 192). A pair of worksheets that summarize most of the content of this section, suitable for
classroom exercises, is located in Appendix D: Worksheets for teaching On the contrary on page
193. These include examples that will permit students to strengthen and confirm their
understanding of the material. A photocopiable table summarizing the points in this section is
presented below (Table 26):
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Table 26: Summary of pedagogical descriptions
Variety of OTC Defining characteristics
Contrastive
Found in a description of the way that two things are opposite. A meaning of on the contrary that is uncommon, but rarer in the
U.S. than in some other English-speaking countries. The meaning is similar to in contrast or on the other hand Very rarely found after a verb phrase Never between the two halves of a compound sentence
(semicolon, OTC, comma) Never at the beginning of a sentence in the U.S., but sometimes
located there in other English-speaking countries. Never after a rhetorical question Frequently after a sentence-initial prepositional phrase or noun
phrase Sometimes immediately after the word or or the word if
(specifically, Alternative on the contrary)
Corrective
Found in a description of the way that one thing is untrue or incorrect while another thing is true or correct.
The most common meaning of on the contrary The meaning is similar to in fact, however… Sometimes after a rhetorical question, or after a sentence that has
a word that makes the sentence “more negative”: few, little, rarely, seldom, barely, hardly, scarcely
Very rarely found after a noun phrase Never immediately after the word or or the word if Never after a prepositional phrase Frequently found between the two halves of a compound
sentence (semicolon, OTC, comma) Frequently found at the beginning of a sentence Frequently after a verb phrase Frequently after a sentence that has a negative word like no, not,
none, wasn’t, hasn’t, didn’t, and so on.
This table can also be used as an answer key to the exercise on page 193.
In addition to being distinguished by a different count of referents (one versus two), the
three varieties are characterized by significantly different discourse functions, syntactic
tendencies, metaphoric imagery, sequential relations and degree of presence in the corpus data.
Educators can fine-tune their different pedagogical approach to account for the interaction of
153
these: a highly simplified approach, which focuses solely on the main variety and its
distinguishing characteristics, might look quite similar to traditional accounts. A more refined
approach, however, can take into account several related factors at once. This section
summarizes those differences and presents a few pedagogical implications.
A common pedagogical approach to defining on the contrary involves asserting that the
Corrective variety is the only acceptable use. It is certainly true that Corrective OTC is by far the
most common form. It is also true that Corrective OTC use is much more firmly established in
the mental dictionary of the average writer, particularly the average writer who is immersed in
U.S. English rather than other English varieties. Generally speaking, however, there are two
main forms: Corrective and Contrastive. The meaning of Corrective OTC is similar to “in fact,
however,” while the meaning of Contrastive OTC is similar to “in contrast” or “on the other
hand.”
A third form, Alternative OTC, is in many ways a subvariety of Contrastive OTC, but it is
different from Contrastive in important details. Although the Contrastive variety is far less
common than the Corrective in both U.S. and non-U.S. English, there is evidence that
Contrastive OTC may be more common in U.K usage than in U.S. English texts. Alternative
OTC is certainly not defunct, but it is rarest among the forms in the COHA corpus.
All forms of OTC are considered examples of “formal English”, and are used mainly in
academic texts or other texts that adopt a formal tone. Moreover, a second reason that on the
contrary is used primarily in academic texts is that its most common use – the Corrective variety
– makes a selection between two ideas, accepting one and rejecting the other. Analyzing
competing ideas and attempting to select the best among them is one of the most fundamental
154
goals of many academic texts, so OTC fits naturally in with the communicative goals of the
academic genre.
OTC is nearly always placed between two statements P1 and P2, although in rare cases
Corrective OTC can stand alone (with no P2) as a bare, three-word interjection that strongly
rejects the truth of a P1 found in prior discourse. Contrastive OTC must have both a P1 and a P2.
For all three varieties, proposition P2 is always located immediately after OTC; P1 is very nearly
always immediately before it (especially in modern usage). In every case, OTC is used to suggest
that two things are somehow “opposite”, but OTC means more than simply “the opposite.”
In the very simplest and clearest terms, the communicative goal that writers have in mind
when they use Corrective on the contrary has two parts:
a) first, mention or note an untrue or incorrect statement or idea, and
b) then replace that statement with a true or correct one.
Corrective OTC is used when the idea that the speaker or writer believes is correct is also
perceived to be somehow completely the opposite of the wrong idea. These two ideas are
completely incompatible with one another and are mutually exclusive, so deciding between them
is like choosing one side or the other of a coin, as illustrated graphically in the image on page
192.
There are two distinct forms of Corrective OTC: one-person and two-person (Fraser,
2009b). The two-person form occurs within a conversation between two or more speakers. In this
case, the first person mentions an idea that the second person considers to be untrue or incorrect.
The second person uses OTC to express opposition, and then corrects the first person’s
statement. In written texts, two-person OTC may sometimes be found in the Fiction genre, as in
example (1), which is duplicated below from an example on page 15:
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(51) “Advertising,” Hunt said thoughtfully, “is the unspeakable expression of an
unspeakable age.”
“On the contrary,” Morgan said, “I adore advertising. It is the only form
of modern art to concern itself, however remotely, with the truth.”
Since a speaker using OTC clearly signals a formal tone, its use is occasionally employed in an
ironic or mildly humorous manner, as in the example above. Similar conversations, which may
be either serious or humorous, might also be found in Nonfiction works such as biographies.
One-speaker OTC is by far the most common form in written texts. Since this form
requires the writer to make a statement and then contradict that same statement (usually in the
very next sentence), he or she must find some way to avoid the appearance of self-contradiction.
This is always done by modifying the first statement in one of three ways: by writing it as a
simple negation involving reprise assertion (the most common approach), a rhetorical question,
or a statement qualified by an approximate negator (Huddleston & Pullum, 2002, p. 815) such as
rarely, seldom, barely, hardly, scarcely, few or little. Several examples are given in Table 1 on
page 16, and the subsequent tables on page 18.
Contrastive OTC, as the name implies, draws a very sharp contrast between two separate
ideas, statements, or objects. The second proposition does not correct or replace the first one:
(52) Nuts have monounsaturated and polyunsaturated fats, and according to the IFIC,
individuals with diets high in these fats enjoy lower levels of bad cholesterol.
Saturated fats, on the contrary, increase "bad," low-density lipoprotein (LDL)
cholesterol.
In this example, the consequences of a diet containing monounsaturated and polyunsaturated fats
are contrasted with those of a diet containing saturated fats. One way in which the
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communicative goal of Contrastive OTC is similar to that of the Corrective variety is that both
are used when the writer intends to express his or her preconception that the two things being set
apart are completely opposite in some particular sense that is important in the current discourse.
This similarity is due to the shared Latin root of the word “contrary” (contrarius “opposite,
opposed”).
The location of OTC within the syntax of a sentence is extremely important in conveying
the correct meaning. Corrective OTC uses one set of syntactic forms, and Contrastive OTC uses
a different set. Overlap is always uncommon, and forms that are strongly associated with one
variety are typically so prohibitively rare in the other variety that using the wrong punctuation
creates a sentence that be considered “wrong” based solely on the syntactic form, in the strictly
empirical sense of running strongly counter to native use as attested in corpus data. These should
never be considered a viable option.
A case in which syntax confuses the meaning of a sentence is found in this example from
a textbook, which was offered as an instance of incorrect use of OTC (Larsen-Freeman and
DeCarrico, 2010):
(53) There are a lot of mountains in the West; on the contrary, there are few in the
Midwest. (p. 33)
This was labeled incorrect/unacceptable in the answer section of the textbook, and was explained
as follows:
[Error in Meaning]. The logical connector ‘on the contrary’ usually denies a proposition.
A connector like ‘in contrast’, to compare two things, would be better. (p. 268)
157
However, the textbook’s explanation is incorrect in a subtle but important manner: OTC is not
strictly limited to the communicative goal of denying a proposition. The biggest problem with
the sentence above is that a syntactic form that is strongly associated with Corrective OTC was
used in a sentence where the meaning requires the Contrastive variety.
Corrective OTC is very strongly associated with three syntactic forms, and use of one of
these forms creates a strong expectation in the reader’s mind that the example of OTC in a
sentence will perform a Corrective function. These forms are as follows:
a) sentence-initial location
b) as a three-word conjunctive adverb (between clauses, preceded by a semicolon and
followed by a comma)
c) following a comma, but after a verb phrase
By far the most common syntactic form for Corrective OTC is sentence-initial location, which
characterizes 59.84% of all the examples of Corrective OTC in all four genres throughout the
whole 200-year span of the corpus. That proportion rises considerably in the modern period.
From 1960 through 2009 there were 867 total examples of Corrective OTC in all four genres in
the COHA corpus data, and no fewer than 669 (77.16%) of those were sentence-initial.
Moreover, a total of 72 (8.3%) of the examples in the same modern period were as a three-word
conjunctive adverb preceded by a semicolon and followed by a comma, which is the structure we
saw in the “a lot of mountains in the West” example in the previous paragraph. Of the remaining
126 examples of Corrective OTC in the modern period, 55 (6.34%) occurred after a verb phrase
and a comma, 42 (4.84%) followed the word but with or without a comma and fell between
clauses or verb phrases, 13 (1.50%) followed the word that with or without a comma and fell
between clauses or verb phrases, seven (0.81%) occurred after a colon or dash and were between
clauses, five (0.58%) occurred after the word no with a comma, and the remaining four (0.81%)
158
were of various forms, including one example following a sentence-initial pronoun and a
comma, and one following a sentence-initial proper noun (the name of the person being
addressed by a speaker). That sentence was in effect an example of sentence-initial OTC,
however, since the name is an interjection rather than the proposition referred to by OTC. All of
this serves to reiterate that the use of one of the three main forms listed above creates a very
potent expectation in the reader’s mind that the example of OTC in the sentence will perform a
Corrective function. Finally, what should not be forgotten is that there are no examples of
Corrective OTC following a prepositional phrase in the COHA corpus since 1960 (and only two
within the entire two centuries that the corpus covers), no examples following a noun phrase of
more than one word, and only one case where OTC refers to a pronoun. The expectations created
by syntactic form will lead the reader to a very reasonable conclusion that the use of OTC is
“wrong” if it follows a preposition phrase or noun phrase, reaffirming the importance of syntax.
At the same time, the forms that are most common for Corrective OTC are extremely
rarely used for Contrastive OTC in the corpus. In the same modern period there were 78
examples of Contrastive (including Alternative) OTC, of which only two (2.6%) were sentence-
initial. Zero tokens were preceded by a semicolon and followed by a comma, which is the form
in the “a lot of mountains in the West” example in the previous paragraph. Very obviously, using
one of those two syntactic forms in a sentence that has a Contrastive communicative goal will be
confusing, and is likely to be considered “wrong” by many readers. That is true at the very least
for U.S. English; the situation may not be as confusing in U.K. usage.
Similar to the strong correspondence between Corrective OTC and three syntactic forms
as described above, Contrastive OTC is strongly associated with two forms:
159
a) sentence-medial, after a short noun phrase that is the topic of the sentence
b) sentence-medial, after short prepositional phrase that includes the topic of the sentence
Of the 78 Contrastive examples in the COHA corpus data since 1960, 59 (75.64%) followed a
short noun phrase that was the topic of the sentence, eight (10.26%) were Alternative OTC, eight
others (10.26%) followed a prepositional phrase, and four (3.85%) followed a verb phrase.
However, two of the three atypical post-VP forms were drawn from English translations of non-
English texts. As a final point, once again the syntactic structures that are commonly found
containing tokens of Contrastive OTC are either rare or virtually non-existent in cases where the
Corrective form is employed. This clear cleavage along semantic lines presents evidence of the
polysemy of OTC.
160
6. Summary and limitations
In contrast with Bell (1998), Fraser (1999) and some authoritative reference works, but in
line with other researchers (notably those contributing to Fischer, 2006a), the semantics of OTC
are analyzed as creating an example of polysemy. More specifically, the discourse particle OTC
is a rather complex (but underspecified) radial network of two discourse devices: images and
constraints. The images, however, bear meaning, or more accurately activate meaning in the
mind of the comprehender. On this view, OTC presents separate definitions derived from
separate metaphoric extensions of its core origin in a locative prepositional phrase. Diachronic
trends in usage are closely linked to cultural dynamics that were discussed within the framework
of a cognitive-semiotic theory. These cultural dynamics are creating pressure that is moving OTC
into a more monosemous state via semantic narrowing, but the Contrastive variety at least is still
attested in the COHA corpus. The conclusion that OTC is polysemous is strongly supported by
both syntactic and usage data, as well as the results of Behavioral Profiles (Gries, 2010; Hanks,
1996) drawing on data from forecast error variance decompositions (FEVDs) ancillary to vector
autoregression systems.
The particle’s conceptual content draws on metaphoric extensions of underlying locative
imagery (Lakoff, 1987; Lakoff & Johnson, 1999). The metaphoric origins of OTC are discussed
within the context of a cognitive-semiotic approach to discourse particles (Hansen, 2006;
Schiffrin, 2006). This theoretical approach outlines the cognitive processes involved in both
synchronic usage and diachronic change. Pedagogical presentation of OTC could include an
appeal to this metaphorical framework in a manner that is intuitive and readily accessible to
students. At least for this one particle, this approach is preferable to more common approaches
that offer either mechanical descriptions of usage conventions or lists of “synonyms” which are
161
not in fact synonymous (Bell, 1998). A similar approach appealing to the metaphorical origins of
a discourse particle may be generalizable for use with other particles as well, although that
possibility was not explored in this analysis.
Drawing conclusions based on actual usage as found within corpus data rather than on
introspected examples offers a more theoretically and empirically sound account. At least in U.S.
English, historical corpus data for OTC offers clear evidence of semantic narrowing, involving
the rejection by U.S. speakers of one of two broad cognitive-semiotic interpretations in favor of
another. Specifically, a Contrastive variety that was not uncommon in U.S. texts of the previous
century has been almost completely abandoned in modern usage, leaving only a Corrective
interpretation (that has always been the more common of the two) to become the default form.
Contrastive OTC has declined to very low usage rates for both NS and NNS in the U.S. corpus
data. Similar written data from synchronic British English corpora suggests, however, that
British English writers have not participated in the narrowing process to quite the same degree. A
related conclusion is that regional variations may lend an added dimension of complexity to any
attempt to define a particle’s semantic content or present overly prescriptive rules for usage.
Finally, Alternative OTC has also declined through time, but at a far steeper rate than the other
two varieties (see Figure 9, on page 93). Meanwhile, at roughly the same time that the
Contrastive variety of OTC was dwindling into very low rates of usage in the U.S. data, tokens
of semantically similar or related discourse particles (in contrast and by contrast) were becoming
increasingly common, to all appearances as a means of compensating for the loss of Contrastive
OTC.
A further finding is that within some genres of U.S.-based publications, the English
variety of the authors the single most important determining factor in usage patterns per 10,000
162
words of text, and the group driving the variability in the data is those whose English variety is
not native to the U.S.. That remains true despite the moderating influence of multiple editors,
presumably chosen not only for knowledge of the relevant topic domain but also for their
expertise in bringing manuscripts into conformance with genre-specific standards U.S. English.
Informally, then, this calls into question the degree to which U.S. English publications accurately
reflect U.S. English usage, or at least make plain the necessity of accounting for native-speaker
background in any finely-granulated analysis.
Corpus-based data reveal a tight correspondence between sentential placement and the
selection of a semantic schema, as summarized in section 4.3 beginning on page 101. Discourse
particles in post-NP and especially post-PP placement consistently fulfill a Contrastive role,
while those in sentence-initial position, or subsequent to a clause or verb phrase, or following the
coordinating conjunctions and and but correlate with a Corrective scheme. The particularly
robust tendency for discourse particles in post-NP placement to consistently fulfill a Contrastive
role is one key finding of this research. This finding adds greater clarity to issues of defining
OTC as well as questions regarding its sentential placement. These issues are commonly found
among student questions that must be addressed in pedagogical presentation, so this present
analysis offers aid to teachers in that respect. At any rate, semantic and syntactic details offered
by this analysis will help teachers refine their explanation of on the contrary to reflect actual
usage rather than the over-simplified explanations typically offered in EFL/ESL textbooks and
other reference sources.
Finally, a set of hypotheses were posited and tested via innovation accounting, with the
understanding that these hypotheses supported a fundamental assertion of polysemy in
Corrective versus Contrastive OTC. These hypotheses could be paraphrased as suggesting that
163
the interaction levels between discourse particles, as described statistically within the FEVDs,
would display a strong, predictable pattern in their relative degree of richness, which could be
ranked by degree of interaction. These rankings would in turn reveal key semantic characteristics
of both Contrastive and Corrective OTC. Twenty four vector autoregression systems (VARs)
were generated and applied to time series data in order to obtain the ancillary forecast error
variance decomposition estimates. A step-by-step logical process was followed to relate the data
to a set of five hypotheses. The results were resoundingly positive. First, and unsurprisingly,
results were poor for both varieties of OTC in the Fiction genre. This falls perfectly in line with a
point repeatedly asserted in theoretical discussion and confirmed both in corpus analysis and by
examining individual FEVDs: the rhetorical goals of the Fiction genre do not provide fertile
ground for use of the rhetorical tool of OTC. Rather than constituting a defeat of the hypotheses,
however, these findings are properly seen as partial evidence that the results FEVD analyses are
reliable, constituting support for the new approach proposed here. One fine-grained conclusion
of the FEVD analysis was that the rhetorical task of correcting requires a more rigorous and
precise selection of words than that of contrasting, and thus for those two tasks the systems of
interaction between discourse particles are structured differently in rational ways. Finally, in the
Nonfiction and Magazine genres, the five hypotheses were confirmed in 19 out of 20 possible
instances. Moreover out of all 24 of rankings of the four particles in the Behavioral Profile
described in Table 25 on page 147, only one can be taken as genuinely counter-intuitive. These
results offer resounding support for the assertion that OTC is polysemous, since words are best
defined by their pattern of interaction with other words.
One limitation of this research is the unavailability of a corpus of non-U.S. English that
covers the same time span and is divided into the same genre categories as the COHA corpus of
164
U.S. English. Data regarding U.K usage is highly suggestive, but lacking a truly diachronic
corpus with a parallel format, firm conclusions are difficult to draw. Further research could
almost certainly benefit from analyzing texts from additional corpora as well.
As a direct result of that limitation, one possible concern with this analysis is an objection
to drawing a dichotomy between native/nonnative speakers, and in particular on restricting the
definition of “native speaker” to native speakers of U.S. English. The corpus-based, pedagogical
section of this research is an objective analysis of observable and quantifiable tendencies
regarding the use and placement of one polysemous discourse particle. It restricts its conclusions
to quantifiable results regarding syntactic structures, and does not prescribe or assign preferred
status or make value judgments regarding any native speaker group or their English variety, nor
does it delve into complex sociolinguistic question of negotiated speaker identities (see for
example Faez, 2011a, Faez, 2011b). “Native speaker” was defined as native speaker of U.S.
English principally as a result of the nature of the COHA database, which strives to collect
tokens of the American English variety. However, the results of this analysis are still quite valid
for corpus analysis and pedagogical discussion.
165
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Appendix A: Summary Tables, all corpora
Table 27: BNC word counts, fiction and non-fiction, written data
YEAR* FICT NF TOT1971 0 74760 747601972 36114 0 36114
1973 33580 109008 142588
1974 0 35356 35356
1975 95300 132365 227665
1976 42767 116713 159480
1977 40945 122716 163661
1978 52552 91172 143724
1979 126624 390453 517077
1980 96451 282303 378754
1981 138480 228374 366854
1982 145951 360664 506615
1983 334124 300494 634618
1984 92843 1402210 1495053
1985 230510 1050792 1281302
1986 285027 1540615 1825642
1987 584161 1879562 2463723
1988 1142927 2541580 3684507
1989 2116619 5644771 7761390
1990 3010472 6079891 9090363
1991 4960918 10068832 15029750
1992 5405958 11494658 16900616
1993 4705815 7007533 11713348
1994 38094 226680 264774
1995 0 173177 173177
1997 0 42918 42918
1998 29629 36816 66445
1999 70818 0 70818
TOT 23816679 51434413 75251092
*(documents for which a date was recorded)
179
Table 28: Total COHA Word count, by genre, by decade
Decade NF FIC MAG NEWS TOTAL
1810 451542 641164 88316 – 1181022
1820s 1461012 3751204 1714789 – 6927005
1830 3038062 7590350 3145575 – 13773987
1840 3641434 8850886 3554534 – 16046854
1850 3155910 9094346 4220558 – 16470814
1860 2902551 9450562 4437941 262198 17053252
1870 2827774 10291968 4452192 1008696 18580630
1880 3262461 11215065 4481568 1322180 20281274
1890 3324755 11252983 4679486 1383948 20641172
1900 3551643 12029439 5062650 1433576 22077308
1910 3534899 11935701 5694710 1414807 22580117
1920 3403149 12498917 5800481 3427812 25130359
1930 3080629 11876996 5908471 3507935 24374031
1940 3056010 11946743 5527688 3497509 24027950
1950 3092375 11986437 5757820 3522545 24359177
1960 3141582 11578880 5634166 3404244 23758872
1970 2961569 11626911 5741323 3383924 23713727
1980 3108775 12152603 5752854 4113254 25127486
1990 3104303 13272162 7440305 4060570 27877340
2000s 3121839 14590078 7620570 4088704 29421191
180
Table 29: Corrective, Contrastive and Alternative OTC per 10K, decennial data
DATE Corrective Contrastive Alternative1810s* 0.2117 0.0339 0.01691820s 0.3306 0.1184 0.03611830s 0.3078 0.1154 0.02251840s 0.2786 0.0810 0.01431850s 0.2696 0.0783 0.01521860s 0.2475 0.0792 0.01231870s 0.2809 0.0732 0.01351880s 0.2194 0.0419 0.00691890s 0.2093 0.0368 0.00531900s 0.1848 0.0394 0.00821910s 0.1931 0.0385 0.00441920s 0.1659 0.0203 0.00521930s 0.1502 0.0271 0.00331940s 0.1452 0.0142 0.00171950s 0.1227 0.0168 0.00211960s 0.1216 0.0118 0.00291970s 0.0848 0.0059 0.00041980s 0.0549 0.0036 0.00081990s 0.0506 0.0039 0.00222000s* 0.0336 0.0017 0.0003* Data from the 1810s and 2000s is incomplete
181
Table 30: Corrective OTC, COHA, raw decennial data
Decade Total FIC FIC NS
FIC NNS
FIC XNS NF NF
NSNF
NNSNF
XNS MAG NEWS
1810** 25 2 2 0 0 20 9 11 0 3 –1820s 229 43 42 0 1 72 41 31 0 114 –1830 424 131 129 2 0 124 78 42 4 169 –1840 449 140 128 11 1 133 99 34 0 176 –1850 445 146 137 9 0 99 76 23 0 200 –1860 422 156 142 11 3 94 47 47 0 156 161870 523 188 166 21 1 117 25 92 0 174 441880 446 156 139 17 0 97 73 24 0 149 441890 433 177 171 5 1 69 43 26 0 154 331900 411 147 130 15 2 74 30 43 1 162 281910 438 123 112 11 0 99 58 39 2 182 341920 418 133 117 16 0 79 57 18 4 132 741930 367 100 90 10 0 73 49 20 4 113 811940 351 103 89 14 0 64 47 16 1 115 691950 299 89 84 4 1 58 31 25 2 77 751960 290 71 67 3 1 80 62 16 2 88 511970 201 46 34 11 1 61 25 34 2 65 291980 138 27 24 3 0 56 22 32 2 45 101990 142 41 22 17 2 52 36 15 1 34 15
2000s** 99 44 35 9 0 31 14 13 4 18 6* One token may be recorded in more than one column, e.g., one token of Fiction (Native Speaker) appears in Total, Fiction, and Fiction Native Speaker** Data from the 1810s and 2000s is incomplete
LegendFIC: FictionNF: NonfictionNS: Native speakerNNS: Non-native speakerXNS: Unknown native languageMAG: MagazineNEWS: News
182
Table 31: Contrastive OTC, COHA, raw decennial data
Decade Total FIC FIC NS
FIC NNS
FIC XNS NF NF
NSNF
NNSNF
XNS MAG NEWS
1810** 3 1 1 0 0 2 1 1 0 0 –1820s 82 11 11 0 0 19 9 9 1 52 –1830 159 30 27 3 0 76 14 60 2 53 –1840 129 52 43 9 0 40 16 23 1 37 –1850 128 28 23 5 0 37 16 21 0 63 –1860 135 33 28 5 0 37 11 26 0 60 51870 135 31 23 8 0 49 6 43 0 47 81880 84 26 25 1 0 19 14 5 0 34 51890 75 19 18 1 0 20 14 6 0 30 61900 84 23 17 6 0 35 12 21 2 25 11910 85 23 22 0 1 26 16 9 1 34 21920 48 9 8 1 0 22 19 3 0 16 11930 65 20 18 2 0 24 14 10 0 16 51940 32 6 6 0 0 15 12 2 1 8 31950 41 7 7 0 0 25 12 9 4 7 21960 27 1 1 0 0 15 12 3 0 9 21970 14 6 5 1 0 4 3 1 0 4 01980 9 1 1 0 0 5 3 2 0 1 21990 11 2 1 0 1 8 3 3 2 1 0
2000s** 5 0 0 0 0 3 1 2 0 2 0* One token may be recorded in more than one column, e.g., one token of Fiction (Native Speaker) appears in Total, Fiction, and Fiction Native Speaker** Data from the 1810s and 2000s is incomplete
LegendFIC: FictionNF: NonfictionNS: Native speakerNNS: Non-native speakerXNS: Unknown native languageMAG: MagazineNEWS: News
183
Table 32: Alternative OTC, COHA, raw decennial data *
Decade Total FIC FIC NS
FIC NNS
FIC XNS NF NF
NSNF
NNSNF
XNS MAG NEWS
1810** 2 0 0 0 0 1 0 1 0 1 –1820s 23 2 2 0 0 8 2 6 0 13 –1830 30 3 3 0 0 17 8 8 1 10 –1840 22 6 4 2 0 11 5 6 0 5 –1850 23 3 3 0 0 12 8 4 0 8 –1860 20 3 3 0 0 10 4 6 0 7 01870 23 5 4 1 0 8 1 7 0 8 21880 12 2 2 0 0 1 0 1 0 6 31890 10 4 4 0 0 2 1 1 0 4 01900 16 6 5 1 0 6 2 4 0 3 11910 10 2 2 0 0 4 1 2 1 3 11920 8 1 1 0 0 4 4 0 0 2 11930 8 0 0 0 0 4 4 0 0 3 11940 4 0 0 0 0 2 2 0 0 2 01950 3 0 0 0 0 3 1 2 0 0 01960 4 0 0 0 0 2 2 0 0 2 01970 1 0 0 0 0 1 1 0 0 0 01980 2 0 0 0 0 2 0 1 1 0 01990 3 0 0 0 0 2 1 1 0 0 1
2000s** 1 1 1 0 0 0 0 0 0 0 0* One token may be recorded in more than one column, e.g., one token of Fiction (Native Speaker) appears in Total, Fiction, and Fiction Native Speaker** Data from the 1810s and 2000s is incomplete
LegendFIC: FictionNF: NonfictionNS: Native speakerNNS: Non-native speakerXNS: Unknown native languageMAG: MagazineNEWS: News
184
Table 33: Corrective per 10K, decennial data *
DATE Total FIC FIC NS FIC NNS FIC XNS NF NF NS NF NNS NF XNS MAG NEWS1810** 0.2117 0.0312 0.0312 0 0 0.4429 0.1993 0.2436 0 0.3397 –1820s 0.3306 0.1146 0.1120 0 0.0442 0.4928 0.2806 0.2122 0 0.6648 –1830 0.3078 0.1726 0.1700 0.0026 0 0.4082 0.2567 0.1382 0.0132 0.5373 –1840 0.2798 0.1582 0.1446 0.0124 0.0108 0.3652 0.2719 0.0934 0 0.4951 –1850 0.2702 0.1605 0.1506 0.0099 0 0.3137 0.2408 0.0729 0 0.4739 –1860 0.2475 0.1651 0.1503 0.0116 0.0346 0.3239 0.1619 0.1619 0 0.3515 0.61021870 0.2815 0.1827 0.1613 0.0204 0.0059 0.4138 0.0884 0.3253 0 0.3908 0.43621880 0.2199 0.1391 0.1239 0.0152 0 0.2973 0.2238 0.0736 0 0.3325 0.33281890 0.2098 0.1573 0.1520 0.0044 0.0054 0.2075 0.1293 0.0782 0 0.3291 0.23841900 0.1862 0.1222 0.1081 0.0125 0.0214 0.2084 0.0845 0.1211 0.0028 0.3200 0.19531910 0.1940 0.1031 0.0938 0.0092 0 0.2801 0.1641 0.1103 0.0057 0.3196 0.24031920 0.1663 0.1064 0.0936 0.0128 0 0.2321 0.1675 0.0529 0.0118 0.2276 0.21591930 0.1506 0.0842 0.0758 0.0084 0 0.2370 0.1591 0.0649 0.0130 0.1913 0.23091940 0.1461 0.0862 0.0745 0.0117 0 0.2094 0.1538 0.0524 0.0033 0.2080 0.19731950 0.1227 0.0743 0.0701 0.0033 0.0079 0.1876 0.1002 0.0808 0.0065 0.1337 0.21291960 0.1221 0.0613 0.0579 0.0026 0.0064 0.2546 0.1974 0.0509 0.0064 0.1562 0.14981970 0.0848 0.0396 0.0292 0.0095 0.0083 0.2060 0.0844 0.1148 0.0068 0.1132 0.08571980 0.0549 0.0222 0.0197 0.0025 0 0.1801 0.0708 0.1029 0.0064 0.0782 0.02431990 0.0509 0.0309 0.0166 0.0128 0.0076 0.1675 0.1160 0.0483 0.0032 0.0457 0.0369
2000s** 0.0335 0.0307 0.0244 0.0063 0 0.1029 0.0465 0.0432 0.0133 0.0235 0.0133* One token may be recorded in more than one column, e.g., one token of Fiction (Native Speaker) appears in Total, Fiction, and Fiction Native Speaker** Data from the 1810s and 2000s is incomplete
LegendFIC: FictionNF: NonfictionNS: Native speakerNNS: Non-native speakerXNS: Unknown native languageMAG: MagazineNEWS: News
185
Table 34: Contrastive per 10K, decennial data*
DATE Total FIC FIC NS FIC NNS FIC XNS NF NF NS NF NNS NF XNS MAG NEWS1810** 0.0254 0.2127 0.2127 0 0 0.0443 0.0221 0.0221 0 0 –1820s 0.1184 0.3063 0.3063 0 0 0.1300 0.0616 0.0616 0.0681 2.9144 –1830 0.1154 0.3318 0.3060 0.0258 0 0.2502 0.0461 0.1975 0.0454 1.7055 –1840 0.0804 0.5464 0.4263 0.1201 0 0.1098 0.0439 0.0632 0.0502 0.9983 –1850 0.0777 0.2890 0.2425 0.0465 0 0.1172 0.0507 0.0665 0 1.5457 –1860 0.0792 0.3034 0.2490 0.0544 0 0.1275 0.0379 0.0896 0 1.3132 1.76061870 0.0727 0.3265 0.2204 0.1062 0 0.1733 0.0212 0.1521 0 1.0621 0.81081880 0.0414 0.2009 0.1908 0.0101 0 0.0582 0.0429 0.0153 0 0.7289 0.39221890 0.0363 0.1825 0.1738 0.0088 0 0.0602 0.0421 0.0180 0 0.6459 0.40461900 0.0380 0.1750 0.1337 0.0413 0 0.0985 0.0338 0.0591 0.0697 0.4947 0.08271910 0.0376 0.1972 0.1901 0 0.0071 0.0736 0.0453 0.0255 0.0205 0.5625 0.19231920 0.0191 0.0251 0.0222 0.0029 0 0.0646 0.0558 0.0088 0 0.2917 0.04201930 0.0267 0.1698 0.1545 0.0153 0 0.0779 0.0454 0.0325 0 0.2695 0.15951940 0.0133 0.0510 0.0510 0 0 0.0491 0.0393 0.0065 0.0274 0.1415 0.07691950 0.0168 0.0642 0.0642 0 0 0.0808 0.0388 0.0291 0.1376 0.1269 0.05641960 0.0114 0.0064 0.0064 0 0 0.0477 0.0382 0.0095 0 0.1754 0.05241970 0.0059 0.0504 0.0426 0.0078 0 0.0135 0.0101 0.0034 0 0.0686 01980 0.0036 0.0086 0.0086 0 0 0.0161 0.0097 0.0064 0 0.0160 0.05351990 0.0039 0.0126 0.0077 0 0.0050 0.0258 0.0097 0.0097 0.0842 0.0141 0
2000s** 0.0017 0 0 0 0 0.0100 0.0033 0.0066 0 0.0200 0* One token may be recorded in more than one column, e.g., one token of Fiction (Native Speaker) appears in Total, Fiction, and Fiction Native Speaker** Data from the 1810s and 2000s is incomplete
LegendFIC: FictionNF: NonfictionNS: Native speakerNNS: Non-native speakerXNS: Unknown native languageMAG: MagazineNEWS: News
186
Table 35: Alternative per 10K, decennial data*
DATE Total FIC FIC NS FIC NNS FIC XNS NF NF NS NF NNS NF XNS MAG NEWS1810** 0.0169 0 0 0 0 0.0221 0 0.0221 0 0.1132 -1820s 0.0332 0.0053 0.0053 0 0 0.0548 0.0137 0.0411 0 0.0758 -1830 0.0218 0.0040 0.0040 0 0 0.0560 0.0263 0.0263 0.0033 0.0318 -1840 0.0137 0.0068 0.0045 0.0023 0 0.0302 0.0137 0.0165 0 0.0141 -1850 0.0140 0.0033 0.0033 0 0 0.0380 0.0253 0.0127 0 0.0190 -1860 0.0117 0.0032 0.0032 0 0 0.0345 0.0138 0.0207 0 0.0158 01870 0.0124 0.0049 0.0039 0.0010 0 0.0283 0.0035 0.0248 0 0.0180 0.01981880 0.0059 0.0018 0.0018 0 0 0.0031 0 0.0031 0 0.0134 0.02271890 0.0048 0.0036 0.0036 0 0 0.0060 0.0030 0.0030 0 0.0085 01900 0.0072 0.0050 0.0042 0.0008 0 0.0169 0.0056 0.0113 0 0.0059 0.00701910 0.0044 0.0017 0.0017 0 0 0.0113 0.0028 0.0057 0.0028 0.0053 0.00711920 0.0032 0.0008 0.0008 0 0 0.0118 0.0118 0 0 0.0034 0.00291930 0.0033 0 0 0 0 0.0130 0.0130 0 0 0.0051 0.00291940 0.0017 0 0 0 0 0.0065 0.0065 0 0 0.0036 01950 0.0012 0 0 0 0 0.0097 0.0032 0.0065 0 0 01960 0.0017 0 0 0 0 0.0064 0.0064 0 0 0.0035 01970 0.0004 0 0 0 0 0.0034 0.0034 0 0 0 01980 0.0008 0 0 0 0 0.0064 0 0.0032 0.0032 0 01990 0.0011 0 0 0 0 0.0064 0.0032 0.0032 0 0 0.0025
2000s** 0.0003 0.0007 0.0007 0 0 0 0 0 0 0 0* One token may be recorded in more than one column, e.g., one token of Fiction (Native Speaker) appears in Total, Fiction, and Fiction Native Speaker** Data from the 1810s and 2000s is incomplete
LegendFIC: FictionNF: NonfictionNS: Native speakerNNS: Non-native speakerXNS: Unknown native languageMAG: MagazineNEWS: News
187
Table 36: BNC and COHA per 10k, Corrective and Contrastive, 1976–1994
BNC COHA BNC/COHA%
Year Corrective Contrastive Corrective Contrastive Corrective Contrastive1976 0.063518 0 0.05069505 0.008449175 125.29% 0.00%
1977 – – 0.109910106 0.008141489 – –
1978 0.05771 0 0.087786807 0 65.74% –
1979 0.022384 0 0.084555946 0.022251565 26.47% 0.00%
1980 0.088022 0 0.058901348 0.007853513 149.44% 0.00%
1981 0.141219 0 0.051703864 0 273.13% –
1982 0.040976 0.020488 0.041189639 0.012356892 99.48% 165.80%
1983 0.017905 0.017905 0.079650874 0.003982544 22.48% 449.59%
1984 0.212011 0.028911 0.06003977 0.004002651 353.12% 722.30%
1985 0.048203 0 0.053589817 0 89.95% –
1986 0.196204 0.033635 0.052769752 0 371.81% –
1987 0.095166 0.004138 0.061039167 0 155.91% –
1988 0.093102 0.013692 0.035864704 0.003984967 259.59% 343.59%
1989 0.081478 0.005014 0.053242946 0.004095611 153.03% 122.42%
1990 0.086934 0.005502 0.046299603 0.033071145 187.76% 16.64%
1991 0.101344 0.007636 0.058304203 0 173.82% –
1992 0.08441 0.002795 0.029426911 0 286.85% –
1993 0.07694 0.001539 0.074840243 0.005476115 102.81% 28.10%
1994 0.186952 0 0.049069078 0 381.00% –
188
Table 37: English variety of authors, totals (Fiction)
Word count Texts
Dec Total Known Unknown Total Known Unknown
1810 641164 353258 (55.10%) 287906 (44.90%) 42 17 (40.48%) 25 (59.52%)
1820s 3751204 3558606 (94.87%) 192598 (5.13%) 89 72 (80.90%) 17 (19.10%)
1830 7590350 7370394 (97.10%) 219956 (2.90%) 177 161 (90.96%) 16 (9.04%)
1840 8850886 8664221 (97.89%) 186665 (2.11%) 239 223 (93.31%) 16 (6.69%)
1850 9094346 8908254 (97.95%) 186092 (2.05%) 144 135 (93.75%) 9 (6.25%)
1860 9450562 9027673 (95.53%) 422889 (4.47%) 243 183 (75.31%) 60 (24.69%)
1870 10291968 9954353 (96.72%) 337615 (3.28%) 207 192 (92.75%) 15 (7.25%)
1880 11215065 10822121 (96.50%) 392944 (3.50%) 257 240 (93.39%) 17 (6.61%)
1890 11252983 10982673 (97.60%) 270310 (2.40%) 259 246 (94.98%) 13 (5.02%)
1900 12029439 11357175 (94.41%) 672264 (5.59%) 268 240 (89.55%) 28 (10.45%)
1910 11935701 10774538 (90.27%) 1161163 (9.73%) 291 250 (85.91%) 41 (14.09%)
1920 12486564 11104838 (88.93%) 1381726 (11.07%) 281 243 (86.48%) 38 (13.52%)
1930 11876996 10255651 (86.35%) 1621345 (13.65%) 533 389 (72.98%) 144 (27.02%)
1940 11946743 10001460 (83.72%) 1945283 (16.28%) 420 305 (72.62%) 115 (27.38%)
1950 11986437 10390529 (86.69%) 1595908 (13.31%) 471 352 (74.73%) 119 (25.27%)
1960 11578880 10325699 (89.18%) 1253181 (10.82%) 404 309 (76.49%) 95 (23.51%)
1970 11626911 9863946 (84.84%) 1762965 (15.16%) 335 260 (77.61%) 75 (22.39%)
1980 12152603 10836695 (89.17%) 1315908 (10.83%) 340 283 (83.24%) 57 (16.76%)
1990 13272162 9002816 (67.83%) 4269346 (32.17%) 1715 1068 (62.27%) 647 (37.73%)
2000s 14590078 9119203 (62.50%) 5470875 (37.50%) 4240 2662 (62.78%) 1578 (37.22%)
189
Table 38: English variety of authors, totals (Non-fiction)
Word count Texts
Dec Tot Known Unknown Tot Known Unknown
1810 451542 239919 (53.13%) 211623 (46.87%) 4 3 (75.00%) 1 (25.00%)
1820s 1461012 1394855 (95.47%) 66157 (4.53%) 31 27 (87.10%) 4 (12.90%)
1830 3082112 2775216 (90.04%) 306896 (9.96%) 67 55 (82.09%) 12 (17.91%)
1840 3641434 3353462 (92.09%) 287972 (7.91%) 68 58 (85.29%) 10 (14.71%)
1850 3155910 3140042 (99.50%) 15868 (0.50%) 53 52 (98.11%) 1 (1.89%)
1860 2902551 2861610 (98.59%) 40941 (1.41%) 51 47 (92.16%) 4 (7.84%)
1870 2827774 2780855 (98.34%) 46919 (1.66%) 48 46 (95.83%) 2 (4.17%)
1880 3262461 3186728 (97.68%) 75733 (2.32%) 55 52 (94.55%) 3 (5.45%)
1890 3324807 3227492 (97.07%) 97315 (2.93%) 66 61 (92.42%) 5 (7.58%)
1900 3551643 2971045 (83.65%) 580598 (16.35%) 98 71 (72.45%) 27 (27.55%)
1910 3534899 2833557 (80.16%) 701342 (19.84%) 110 82 (74.55%) 28 (25.45%)
1920 3403149 2508166 (73.70%) 894983 (26.30%) 103 63 (61.17%) 40 (38.83%)
1930 3080629 2166677 (70.33%) 913952 (29.67%) 101 65 (64.36%) 36 (35.64%)
1940 3056010 2250112 (73.63%) 805898 (26.37%) 93 64 (68.82%) 29 (31.18%)
1950 3048325 1970102 (64.63%) 1078223 (35.37%) 95 56 (58.95%) 39 (41.05%)
1960 3141582 1849169 (58.86%) 1292413 (41.14%) 96 52 (54.17%) 44 (45.83%)
1970 2961569 1965519 (66.37%) 996050 (33.63%) 88 46 (52.27%) 42 (47.73%)
1980 3108775 1917869 (61.69%) 1190906 (38.31%) 97 56 (57.73%) 41 (42.27%)
1990 3104303 1484422 (47.82%) 1619881 (52.18%) 365 135 (36.99%) 230 (63.01%)
2000s 3121839 1382727 (44.29%) 1739112 (55.71%) 941 441 (46.87%) 500 (53.13%)
190
Appendix B: Texts from COHA corpus deleted from this analysis
A small number of texts which included tokens of OTC were wholly removed from
consideration in this analysis. Most of these are reprinted versions of texts that were originally
published outside of the date range of the COHA corpus (that is, prior to 1810). The publication
dates in the table below are those provided by COHA, and so are often reprint dates rather than
dates of original publication. An additional few sources were excluded for other technical
reasons, as for example duplicate texts (Table 39, below):
Table 39: COHA texts excluded from analysis
Date Word count Genre Title Author(s)
1817 211623 NF
The Federalist, on the new Constitution, written in 1788, by Mr. Hamilton, Mr. Madison, and Mr. Jay: with an appendix, containing the letters of Pacific
Alexander Hamilton, James Madison, John Jay
1836 65173 NF
Yaradee: a plea for Africa, in familiar conversations on the subject of slavery and colonization
Frederick Freeman
1843 6652 FIC The Celestial Rail-road Hawthorne, Nathaniel, 1804-18641864 69928 NF The Federalist Paper Jay, John, 1745-18291876 62659 NF Winter Sunshine Burroughs, John, 1837-19211890 18655 FIC The Merchant of Venice Shakespeare, William, 1564-16161896 18631 FIC Old Christmas Irving, Washington, 1783-18591897 89437 FIC Wolfville Lewis, Alfred Henry, 1857-19141897 26476 FIC Sandra Belloni — Volume 2 Meredith, George, 1828-1909
1903 73658 NF The Philippine Islands, 1493-1898 (Vol 27 of 55) 1636-37 Robertson, James Alexander, 1873-1939
1910 70247 NF The World's Greatest Books -- Volume 15 -- Science Mee, Arthur, 1875-1943
1910 70669 NFThe World's Greatest Books -- Volume 14 -- Philosophy and Economics
Various
1922 23613 FIC The Merchant of Venice: an adaptation Belasco, David, 1853-1931
1923 21356 NFThe Ten Pleasures of Marriage and The Confession of the New Married Couple
Aphra Behn
1937 22995 NF Foods America Gave the World A. Hyatt Verrill1956 36659 NF Engineering in History Richard Shelton Kirby, et al.1972 48231 FIC The Flame and the Flower Kathleen E. Woodiwiss
191
Appendix C: Image for teaching Corrective On the contrary
Figure 27: Image for teaching Corrective On the contrary
192
Appendix D: Worksheets for teaching On the contrary
Do these describe corrective or contrastive on the contrary? Mark CORR or CONT in the blank:
1) ______
Very rarely found after a verb phrase
2) ______
A meaning of on the contrary that is uncommon, but rarer in the U.S. than in some
other English-speaking countries.
3) ______
Very rarely found after a noun phrase
4) ______
The most common meaning of on the contrary
5) ______
The meaning is similar to in fact, however…
6) ______
The meaning is similar to in contrast or on the other hand
7) ______
Sometimes immediately after the word or or the word if
8) ______
Sometimes after a sentence that has a word that makes the sentence “more
negative”: few, little, rarely, seldom, barely, hardly, scarcely
9) ______
Sometimes after a rhetorical question
10) ______
Never immediately after the word or or the word if
11) ______
Never between the two halves of a compound sentence (semicolon, OTC, comma)
12) ______
Never at the beginning of a sentence in the U.S., but sometimes there in other
English-speaking countries.
13) ______
Never after a rhetorical question
14) ______
Never after a prepositional phrase
15) ______
Frequently found between the two halves of a compound sentence (semicolon,
OTC, comma)
16) ______
Frequently at the beginning of a sentence
17) Frequently after a verb phrase
18) ______
Frequently after a sentence that has a negative word like no, not, none, wasn’t,
hasn’t, didn’t, and so on.
193
19) ______
Frequently after a prepositional phrase at the beginning of a sentence
20) ______
Frequently after a noun phrase
21) ______
Found in a description of the way that two things are opposite.
22) ______
Found in a description of the way that one thing is untrue or incorrect while another
thing is true or correct.
Are these quotes examples of corrective or contrastive on the contrary? Mark CORR or CONT
in the blank:
1) ______
What the Chinese parent is conveying to the child is not that “you've got to get A's
in school or else I won't like you.” On the contrary, it’s, “I believe in you so much, I
know that you can be excellent.” (Amy Chua)
2) ______
We do not live to think, but, on the contrary, we think in order that we may succeed
in surviving. (Jose Ortega y Gasset)
3) ______
Truth, like light, blinds. Falsehood, on the contrary, is a beautiful twilight that
enhances every object. (Albert Camus)
4) ______
True love is not a strong, fiery, impetuous passion. It is, on the contrary, an
element calm and deep (Ellen G. White)
5) ______
The common idea that success spoils people by making them vain, egotistic and
self-complacent is erroneous; on the contrary it makes them, for the most part,
humble, tolerant and kind (W. Somerset Maugham)
6) ______
Never walk away from failure. On the contrary, study it carefully and imaginatively
for its hidden assets. (Michael Korda)
7) ______
She was, however, no longer joyous and alert, as on the previous days, but, on
the contrary, overcome with grief. (Alexandre Dumas)
8) ______
I don't think the world will destroy itself in a nuclear cataclysm. On the contrary, we
have the capacity to save ourselves and save the planet, and we will use it.
(Isabel Allende)
9) ______
I am truly free only when all human beings, men and women, are equally free. The
freedom of other men, instead of limiting my freedom, is, on the contrary, its
necessary premise and confirmation. (Mikhail Bakunin)
194
10) ______
Don't flatter yourselves that friendship authorizes you to say disagreeable things
to your intimates. On the contrary, the nearer you come into relation with a person,
the more necessary tact and courtesy become. (Oliver Wendell Holmes, Sr.)
195
Appendix E: Typical Python program by author (corpus data processing)
# -*- coding: utf-8 -*-"""Created on Mon Mar 24 19:53:49 2014@author: Timothy M. Nall
(The author wrote scores of Python programs for various tasks related to text, html and xml searching, data processing and manipulation, and so on. This program is typical, and is presented for illustrative purposes.)
"""crt = "\n"tab = "\t"inby_readfile=open("/coha/summdata/inby_otoh/___new_allinby_in_withnnst.txt","r")summfile=open("/coha/summdata/summdata/___coha__summarydata_newest5.txt","r")wordcountfile=open("/coha/summdata/wcnt/wordcounts_tot_1810ff.txt","r")inby_yr_rawfile=open("/CSV/csv/redo_inby_yr_raw.csv","w")inby_yr_10kfile=open("/CSV/csv/redo_inby_yr_10k.csv","w")the_file=open("/CSV/csv/the_inby.txt","w")###################################################################
declist=[]yrlist=[]for yr in range(1810,2010): yr = `yr` yrlist.append(yr) if yr[3] == "0": dec = yr[:3] declist.append(dec) declen=len(declist)yrlen=len(yrlist)
wordcntlist=[]deleted_ids=[]for line in wordcountfile: line=line.replace(crt,"") wordcntlist.append(line)
196
inbyhdr=["BOTH","BOTHFIC","BOTHMAG","BOTHNEWS","BOTHNF","BY","BYFIC","BYMAG","BYNEWS","BYNF","IN","INFIC","INMAG","INNEWS","INNF"] mainlen=len(inbyhdr)
yr_matrix_10k = [[0 for x in xrange(mainlen)] for x in xrange(yrlen)]yr_matrix_raw = [[0 for x in xrange(mainlen)] for x in xrange(yrlen)]dec_matrix_10k = [[0 for x in xrange(mainlen)] for x in xrange(yrlen)]dec_matrix_raw = [[0 for x in xrange(mainlen)] for x in xrange(yrlen)]
inlist=["in such contrast", "In contrast","; in contrast", "in contrast","In way of contrast"," in way of contrast"]bylist=["By contrast","; by contrast"," by way of contrast","By way of contrast","by contrast"]insuchlist=["in such striking contrast", "in such broad contrast", "in such complete contrast", "in such a contrast", "in such beautiful contrast", "in such ludicrous contrast", "in such marked contrast", "in such agreeable contrast", "in such sharp contrast","in such interesting contrast","in such shocking contrast", "in such strange contrast", "in such vivid, glowing contrast", "in such glaring contrast", "in such unworthy contrast", "in such blessed contrast", "in such startling contrast", "in such violent contrast", "in such comical contrast", "in such ugly contrast", "in such black contrast", "in such singular contrast", "in such great contrast", "in such enormous contrast", "in such pleasing contrast", "in such stark contrast"]
broad_def=1if broad_def==0: inlist=["In contrast","in contrast", "in complete contrast"] bylist=["By contrast","by contrast","by way of contrast","By way of contrast","by contrast"]thecnt=0for line in inby_readfile: theflg=0 fndflg=0 inflg=0 byflg=0 line2=line.replace(crt,"") s = line2.split(tab) yr = s[0].strip() yrpos = yrlist.index(yr)
197
genre = s[1].strip() txt = s[2].strip() natvar = s[3].strip() if line2.find("the contrast") > -1: ### does not resemble discourse particle fndflg = 1 ### semantics, so skip thecnt += 1 theflg=1 the_file.write(line) s2 = txt.split(" ") scnt = -1 if fndflg == 0: for item in s2: scnt += 1 if item == "contrast" or item == "contrast,": if s2[scnt-2] == "in" or s2[scnt-2] == "In": inflg=1 fndflg=1 if s2[scnt-2] == "by" or s2[scnt-2] == "By": byflg=1 fndflg=1 if inflg == 0: for initem in inlist: if initem in line: inflg = 1 fndflg=1 for initem in insuchlist: if initem in line: inflg = 1 fndflg=1 if byflg == 0: for byitem in bylist: if byitem in line: byflg = 1 fndflg=1 if fndflg == 1: if theflg==0: tagpos1=inbyhdr.index("BOTH" + genre) yr_matrix_raw[yrpos][0] += 1 yr_matrix_raw[yrpos][tagpos1] += 1 if inflg == 1: tag = "IN" tagpos2=inbyhdr.index(tag) tag += genre tagpos3 = inbyhdr.index(tag) yr_matrix_raw[yrpos][tagpos2] += 1
198
yr_matrix_raw[yrpos][tagpos3] += 1 elif byflg == 1: tag = "BY" tagpos2=inbyhdr.index(tag) tag += genre tagpos3 = inbyhdr.index(tag) yr_matrix_raw[yrpos][tagpos2] += 1 yr_matrix_raw[yrpos][tagpos3] += 1 else: print linedelim = "," ######## CSV FILEhdtxt='YEAR' + delimfor item in inbyhdr: hdtxt += item + delimhdtxt=hdtxt.strip()inby_yr_rawfile.write(hdtxt + crt)inby_yr_10kfile.write(hdtxt + crt)hdtxt=''
raw_outstr=''outstr_10k = ''col_list=["DUMMYTEXTFORYEARLABEL", "FIC","NF","MAG","NEWS"] ## order of columns in wordcount filefor yrpos in range(yrlen): wdstr = wordcntlist[yrpos] if yrpos >= 5 and yrpos <= 11: print wdstr s_wdstr = wdstr.split(tab) val = yrpos + 1810 ## 1810 is the first year in the corpus; yr = 0,1,2,3 etc. raw_outstr = `val` raw_outstr += delim outstr_10k=raw_outstr ### the same so far, different later for inbyhdrpos in range(mainlen): inbyval= yr_matrix_raw[yrpos][inbyhdrpos] raw_outstr += `inbyval` + delim ### find appropriate wordcount value for genre, given year inbyhdr_str=inbyhdr[inbyhdrpos] colpos=0 ## value if it is not found in for loop below for colitem in col_list: if colitem in inbyhdr_str: break else: colpos += 1
199
wordcount = s_wdstr[colpos] wordcount = float(wordcount) inbyval=float(inbyval) *10000 #### incidence per 10k words inbyval = inbyval / wordcount inbyval = "{:.10f}".format(inbyval) outstr_10k += inbyval + delim raw_outstr = raw_outstr.strip() outstr_10k = outstr_10k.strip() inby_yr_rawfile.write(raw_outstr + crt) inby_yr_10kfile.write(outstr_10k + crt) raw_outstr='' outstr_10k=''
the_file.close()inby_yr_rawfile.close()inby_yr_10kfile.close()cohafile.close()inby_readfile.close()summfile.close()wordcountfile.close()
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Appendix F: Typical hansl script by author (statistics: unit roots, FEVDs, IRFs)
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# this hansl script (for the gretl statistical software package)# written by the author# first performs a KPSS (Kwiatkowski–Phillips–Schmidt–Shin, 1992) test# on the variables to check for a unit root. # If the results are significant at 5%, first differenced data is used,# else data is used in its levels.# A time series graph of each variable was visually inspected for presence of a trend# The script then generates Vector autoregression (VAR) models# and outputs the relevant forecast error variance decomposition matrices (FEVDs) # and impulse response functions (IRFs)
open /CSV/csv/_____varfile.csv smpl 1850 1864loop i = 0..3
list tstx0 = null list tstx1 = null list tstx2 = null list tstx3 = null list tstx4 = null list tstx5 = null list tstx6 = null list tstx7 = null list tstx8 = null list tstx9 = null list tstx10 = null list tstx11 = null list tstx12 = null list tstx13 = null list tstx14 = null list tstx15 = null list tstx16 = null list tstx17 = null list tstx18 = null
## 1) FIC_FAC ## 2) FIC_INC ## 3) FICCorr ## 4) FIChwvr_init ## 5) FICCont ## 6) FIC_otoh ## 7) MAG_FAC ## 8) MAG_INC ## 9) MAGCorr ## 10) MAGhwvr_init ## 11) MAGCont
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## 12) MAG_otoh ## 13) NF_FAC ## 14) NF_INC ## 15) NFCorr ## 16) NFhwvr_init ## 17) NFCont ## 18) NF_otoh ## 19) d_FIC_FAC ## 20) d_FIC_INC ## 21) d_FICCorr ## 22) d_FIChwvr_init ## 23) d_FICCont ## 24) d_FIC_otoh ## 25) d_MAG_FAC ## 26) d_MAG_INC ## 27) d_MAGCorr ## 28) d_MAGhwvr_init ## 29) d_MAGCont ## 30) d_MAG_otoh ## 31) d_NF_FAC ## 32) d_NF_INC ## 33) d_NFCorr ## 34) d_NFhwvr_init ## 35) d_NFCont## 36) d_ NF_otoh
kpss 2 1 --quietif $test < kpsscrit(15,0)[2]tstx1 = 1elsetstx1 = 19endif
kpss 2 2 --quietif $test < kpsscrit(15,0)[2]tstx2 = 2elsetstx2 = 20endif
kpss 2 3 --quietif $test < kpsscrit(15,0)[2]tstx3 = 3elsetstx3 = 21endif
kpss 2 4 --quietif $test < kpsscrit(15,0)[2]tstx4 = 4else
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tstx4 = 22endif
kpss 2 5 --quietif $test < kpsscrit(15,0)[2]tstx5 = 5elsetstx5 = 23endif
kpss 2 6 --quietif $test < kpsscrit(15,0)[2]tstx6 = 6elsetstx6 = 24endif
kpss 2 7 --quietif $test < kpsscrit(15,0)[2]tstx7 = 7elsetstx7 = 25endif
kpss 2 8 --quietif $test < kpsscrit(15,0)[2]tstx8 = 8elsetstx8 = 26endif
kpss 2 9 --quietif $test < kpsscrit(15,0)[2]tstx9 = 9elsetstx9 = 27endif
kpss 2 10 --quietif $test < kpsscrit(15,0)[2]tstx10 = 10elsetstx10 = 28endif
kpss 2 11 --quietif $test < kpsscrit(15,0)[2]tstx11 = 11elsetstx11 = 29endif
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kpss 2 12 --quietif $test < kpsscrit(15,0)[2]tstx12 = 12elsetstx12 = 30endif
kpss 2 13 --quietif $test < kpsscrit(15,0)[2]tstx13 = 13elsetstx13 = 31endif
kpss 2 14 --quietif $test < kpsscrit(15,0)[2]tstx14 = 14elsetstx14 = 32endif
kpss 2 15 --quietif $test < kpsscrit(15,0)[2]tstx15 = 15elsetstx15 = 33endif
kpss 2 16 --quietif $test < kpsscrit(15,0)[2]tstx16 = 16elsetstx16 = 34endif
kpss 2 17 --quietif $test < kpsscrit(15,0)[2]tstx17 = 17elsetstx17 = 35endif
kpss 2 17 --quietif $test < kpsscrit(15,0)[2]tstx18 = 18elsetstx18 = 36endif
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var 1 tstx5 tstx4 tstx6 tstx2 tstx1 --quiet --variance-decomp --impulse-responses var 1 tstx3 tstx4 tstx6 tstx2 tstx1 --quiet --variance-decomp --impulse-responses var 1 tstx11 tstx10 tstx12 tstx8 tstx7 --quiet --variance-decomp --impulse-responses var 1 tstx9 tstx10 tstx12 tstx8 tstx7 --quiet --variance-decomp --impulse-responses var 1 tstx17 tstx16 tstx18 tstx14 tstx13 --quiet --variance-decomp --impulse-responses var 1 tstx15 tstx16 tstx18 tstx14 tstx13 --quiet --variance-decomp --impulse-responses
smpl +15 +15 endloop
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Appendix G: Forecast error decompositions of variance (1850–1864)
Full data range: 1815 - 2009 (n = 195)Current sample: 1850 - 1864 (n = 15)
VAR system, lag order 1OLS estimates, observations 1851-1864 (T = 14)Log-likelihood = 140.33454Determinant of covariance matrix = 1.3520205e-015AIC = -15.7621BIC = -14.3927HQC = -15.8888Portmanteau test: LB(3) = 77.8272, df = 50 [0.0071]
Table 40: Decomposition of variance for FICCont (1851-1864)
period std. error FICCont FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0197546 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0205707 94.9253 2.0193 0.4934 2.2910 0.2710
3 0.0210352 92.2018 2.3607 0.4923 3.2672 1.6779
4 0.0211153 91.7464 2.3429 0.6848 3.4759 1.7500
5 0.0211483 91.5081 2.3494 0.7821 3.4711 1.8892
6 0.0211506 91.4935 2.3570 0.7847 3.4708 1.8940
7 0.0211515 91.4876 2.3603 0.7847 3.4708 1.8967
8 0.021152 91.4857 2.3606 0.7851 3.4719 1.8968
9 0.0211521 91.4850 2.3606 0.7855 3.4719 1.8970
10 0.0211521 91.4847 2.3606 0.7856 3.4719 1.8971
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VAR system, lag order 1OLS estimates, observations 1851-1864 (T = 14)Log-likelihood = 124.57211Determinant of covariance matrix = 1.2850407e-014AIC = -13.5103BIC = -12.1409HQC = -13.6371Portmanteau test: LB(3) = 84.6508, df = 50 [0.0016]
Table 41: Decomposition of variance for FICCorr (1851-1864)
period std. error FICCorr FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0574622 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0686834 77.1108 3.5581 0.4199 18.5581 0.3531
3 0.0765693 63.3627 10.4736 0.4113 19.3921 6.3604
4 0.0802454 57.6993 14.2226 0.3766 18.9557 8.7458
5 0.0816259 56.1946 15.3751 0.4840 18.3983 9.5479
6 0.0819933 56.0978 15.4223 0.6181 18.2934 9.5684
7 0.0821778 56.0293 15.3676 0.6925 18.3612 9.5494
8 0.0823338 55.8459 15.4254 0.7095 18.4000 9.6192
9 0.0824297 55.7161 15.4973 0.7086 18.3908 9.6873
10 0.082467 55.6756 15.5264 0.7093 18.3766 9.7121
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VAR system, lag order 1OLS estimates, observations 1852-1864 (T = 13)Log-likelihood = 102.76043Determinant of covariance matrix = 9.3699627e-014AIC = -11.1939BIC = -9.8902HQC = -11.4619Portmanteau test: LB(3) = 77.7329, df = 50 [0.0072]
Table 42: Decomposition of variance for MAGCont (1851-1864)
period std. error MAGCont d_MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.047133 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0545796 75.4674 8.6722 15.4949 0.0298 0.3357
3 0.0569524 70.0498 14.1758 14.4483 0.8563 0.4697
4 0.0582726 66.9516 13.5408 15.3819 3.6757 0.4501
5 0.0597206 63.8873 13.0415 14.6591 5.0377 3.3744
6 0.0605788 62.1167 15.1105 14.2498 5.1277 3.3953
7 0.0609052 61.4774 15.4390 14.1717 5.0917 3.8201
8 0.0613518 60.5900 15.6119 14.1303 5.2085 4.4593
9 0.0617427 59.8306 16.5476 13.9554 5.2088 4.4576
10 0.0618747 59.5853 16.6478 13.9719 5.1870 4.6080
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VAR system, lag order 1OLS estimates, observations 1852-1864 (T = 13)Log-likelihood = 84.176727Determinant of covariance matrix = 1.6345541e-012AIC = -8.3349BIC = -7.0312HQC = -8.6029Portmanteau test: LB(3) = 68.4031, df = 50 [0.0429]
Table 43: Decomposition of variance for MAGCorr (1851-1864)
period std. error MAGCorr d_MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.141897 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.155829 88.1904 2.7807 2.1535 5.7640 1.1114
3 0.16924 75.1979 2.9090 5.9654 11.2128 4.7149
4 0.175205 72.6679 5.5780 5.9045 10.7787 5.0711
5 0.17653 71.5836 5.9104 5.8760 10.6872 5.9428
6 0.17907 69.9470 6.8453 5.9909 10.7612 6.4555
7 0.180293 69.6278 7.3678 5.9109 10.6438 6.4496
8 0.180991 69.1150 7.3835 5.9541 10.5661 6.9812
9 0.182112 68.4670 7.8805 5.9718 10.5297 7.1510
10 0.182642 68.3434 8.0873 5.9373 10.4946 7.1374
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VAR system, lag order 1OLS estimates, observations 1851-1864 (T = 14)Log-likelihood = 56.044797Determinant of covariance matrix = 2.2934702e-010AIC = -3.7207BIC = -2.3513HQC = -3.8474Portmanteau test: LB(3) = 67.5507, df = 50 [0.0496]
Table 44: Decomposition of variance for NFCont (1851-1864)
period std. error NFCont NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.0897554 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.103156 78.3022 8.3753 12.8748 0.0026 0.4451
3 0.105107 76.0116 9.1269 12.6673 0.6861 1.5081
4 0.105966 75.0307 9.3237 12.4736 0.7139 2.4582
5 0.106123 74.8831 9.2985 12.5124 0.7996 2.5065
6 0.106153 74.8540 9.3021 12.5324 0.8052 2.5063
7 0.106156 74.8516 9.3015 12.5344 0.8052 2.5073
8 0.106157 74.8506 9.3016 12.5343 0.8052 2.5083
9 0.106157 74.8503 9.3016 12.5344 0.8052 2.5085
10 0.106157 74.8502 9.3016 12.5344 0.8053 2.5085
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VAR system, lag order 1OLS estimates, observations 1851-1864 (T = 14)Log-likelihood = 60.895043Determinant of covariance matrix = 1.1470273e-010AIC = -4.4136BIC = -3.0442HQC = -4.5403Portmanteau test: LB(3) = 74.0446, df = 50 [0.0152]
Table 45: Decomposition of variance for NFCorr (1851-1864)
period std. error NFCorr NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.107514 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.129016 78.4278 10.6901 0.3819 9.0827 1.4176
3 0.145406 67.9627 15.9322 0.9590 9.5023 5.6438
4 0.146663 67.7279 16.1147 1.1052 9.3466 5.7056
5 0.147012 67.5513 16.0476 1.1340 9.3634 5.9039
6 0.147544 67.4742 16.0770 1.1431 9.4050 5.9007
7 0.147594 67.4309 16.1125 1.1452 9.4099 5.9015
8 0.147617 67.4098 16.1213 1.1460 9.4074 5.9156
9 0.147627 67.4118 16.1193 1.1462 9.4078 5.9148
10 0.147629 67.4111 16.1197 1.1463 9.4083 5.9147
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Appendix H: Forecast error decompositions of variance (1865–1879)
Full data range: 1815 - 2009 (n = 195)Current sample: 1865 - 1879 (n = 15)
VAR system, lag order 1OLS estimates, observations 1865-1879 (T = 15)Log-likelihood = 145.12533Determinant of covariance matrix = 2.7165092e-015AIC = -15.3500BIC = -13.9339HQC = -15.3651Portmanteau test: LB(3) = 74.5678, df = 50 [0.0137]
Table 46: Decomposition of variance for FICCont (1865-1879)
period std. error FICCont FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0208675 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0216792 93.1795 2.1055 0.5321 3.4133 0.7695
3 0.0221867 90.1292 3.4952 1.8048 3.7145 0.8564
4 0.0223887 89.0594 3.9475 2.0366 3.6477 1.3088
5 0.0224449 88.7603 4.1753 2.0298 3.7196 1.3149
6 0.0224628 88.6260 4.2557 2.0266 3.7383 1.3534
7 0.0224702 88.5715 4.2987 2.0257 3.7418 1.3622
8 0.0224733 88.5473 4.3146 2.0260 3.7438 1.3683
9 0.0224747 88.5372 4.3220 2.0259 3.7453 1.3696
10 0.0224752 88.5328 4.3250 2.0258 3.7457 1.3707
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VAR system, lag order 1OLS estimates, observations 1865-1879 (T = 15)Log-likelihood = 128.19802Determinant of covariance matrix = 2.5953901e-014AIC = -13.0931BIC = -11.6770HQC = -13.1082Portmanteau test: LB(3) = 77.6153, df = 50 [0.0074]
Table 47: Decomposition of variance for FICCorr (1865-1879)
period std. error FICCorr FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0561476 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0699912 85.2713 1.0494 9.9981 2.8024 0.8787
3 0.0717711 81.1661 2.7498 9.5348 4.4569 2.0924
4 0.0724383 79.7805 2.7596 11.0122 4.3766 2.0711
5 0.0727318 79.6161 2.7376 11.0329 4.5586 2.0548
6 0.0727778 79.5221 2.7613 11.0683 4.5731 2.0751
7 0.0728028 79.4700 2.7633 11.1115 4.5797 2.0755
8 0.0728086 79.4653 2.7633 11.1099 4.5859 2.0756
9 0.0728109 79.4614 2.7633 11.1140 4.5856 2.0757
10 0.0728116 79.4600 2.7635 11.1144 4.5863 2.0758
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VAR system, lag order 1OLS estimates, observations 1865-1879 (T = 15)Log-likelihood = 111.38779Determinant of covariance matrix = 2.441263e-013AIC = -10.8517BIC = -9.4356HQC = -10.8668Portmanteau test: LB(3) = 83.6319, df = 50 [0.0020]
Table 48: Decomposition of variance for MAGCont (1865-1879)
period std. error MAGCont MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.0504079 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0538593 87.6311 3.6399 0.6197 0.1167 7.9925
3 0.0544135 86.7861 3.5777 1.5153 0.2728 7.8481
4 0.0545388 86.8123 3.5895 1.5084 0.2777 7.8121
5 0.0545707 86.8075 3.5853 1.5204 0.2818 7.8049
6 0.0545741 86.8005 3.5861 1.5206 0.2818 7.8110
7 0.0545742 86.8003 3.5861 1.5208 0.2818 7.8110
8 0.0545743 86.8001 3.5862 1.5208 0.2818 7.8111
9 0.0545743 86.8001 3.5862 1.5209 0.2818 7.8111
10 0.0545743 86.8001 3.5862 1.5209 0.2818 7.8111
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VAR system, lag order 1OLS estimates, observations 1865-1879 (T = 15)Log-likelihood = 112.41912Determinant of covariance matrix = 2.127621e-013AIC = -10.9892BIC = -9.5731HQC = -11.0043Portmanteau test: LB(3) = 86.4182, df = 50 [0.0011]
Table 49: Decomposition of variance for MAGCorr (1865-1879)
period std. error MAGCorr MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.0824723 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0896073 85.8306 0.6036 2.0941 6.0935 5.3782
3 0.0918812 82.4075 1.0490 2.3629 5.9853 8.1953
4 0.0932575 81.7189 1.2397 2.5137 6.4695 8.0583
5 0.0935579 81.2374 1.3974 2.5165 6.8420 8.0067
6 0.0938806 81.0214 1.5005 2.4996 6.9323 8.0462
7 0.0940662 80.8573 1.5580 2.5110 7.0565 8.0173
8 0.094156 80.7634 1.5989 2.5082 7.1194 8.0100
9 0.0942265 80.7125 1.6236 2.5070 7.1554 8.0015
10 0.0942666 80.6757 1.6391 2.5073 7.1829 7.9951
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VAR system, lag order 1OLS estimates, observations 1865-1879 (T = 15)Log-likelihood = 61.915711Determinant of covariance matrix = 1.7878923e-010AIC = -4.2554BIC = -2.8393HQC = -4.2705Portmanteau test: LB(3) = 75.0174, df = 50 [0.0126]
Table 50: Decomposition of variance for NFCont (1865-1879)
period std. error NFCont NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.0921561 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0957949 97.4431 0.2521 1.0464 1.2523 0.0061
3 0.0979464 93.3903 1.7776 1.5172 3.3088 0.0061
4 0.0983431 93.1934 1.8228 1.6047 3.3625 0.0167
5 0.0985964 92.7362 1.9268 1.6779 3.6423 0.0167
6 0.0986258 92.7148 1.9360 1.6827 3.6466 0.0198
7 0.0986518 92.6671 1.9422 1.6934 3.6773 0.0200
8 0.0986538 92.6647 1.9432 1.6934 3.6783 0.0205
9 0.0986559 92.6607 1.9434 1.6946 3.6807 0.0206
10 0.0986561 92.6603 1.9435 1.6947 3.6809 0.0207
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VAR system, lag order 1OLS estimates, observations 1865-1879 (T = 15)Log-likelihood = 48.973054Determinant of covariance matrix = 1.0041486e-009AIC = -2.5297BIC = -1.1136HQC = -2.5448Portmanteau test: LB(3) = 65.5429, df = 50 [0.0691]
Table 51: Decomposition of variance for NFCorr (1865-1879)
period std. error NFCorr NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.186796 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.239883 76.2556 7.5198 10.6642 2.5935 2.9668
3 0.259384 71.9789 6.4967 14.3598 4.6092 2.5554
4 0.267196 68.0553 9.6606 13.8245 5.8203 2.6394
5 0.269761 67.1052 10.2076 14.0193 6.0240 2.6439
6 0.271818 66.5594 10.3596 14.3242 6.1526 2.6042
7 0.272795 66.1282 10.5554 14.2352 6.4884 2.5928
8 0.27294 66.0950 10.5443 14.2792 6.4860 2.5956
9 0.273143 66.0377 10.5526 14.2870 6.5309 2.5918
10 0.27317 66.0255 10.5535 14.2841 6.5448 2.5921
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Appendix I: Forecast error decompositions of variance (1880–1894)
Full data range: 1815 - 2009 (n = 195)Current sample: 1880 - 1894 (n = 15)
VAR system, lag order 1OLS estimates, observations 1880-1894 (T = 15)Log-likelihood = 146.78264Determinant of covariance matrix = 2.1779244e-015AIC = -15.5710BIC = -14.1549HQC = -15.5861Portmanteau test: LB(3) = 73.9842, df = 50 [0.0154]
Table 52: Decomposition of variance for FICCont (1880 – 1894)
period std. error FICCont FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0138571 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0141175 97.1907 0.3351 0.2541 0.7701 1.4500
3 0.0143074 94.6563 1.8453 0.2741 0.7525 2.4718
4 0.0144246 93.1249 2.2255 1.2112 0.8232 2.6152
5 0.0144481 92.8628 2.3199 1.2809 0.8492 2.6873
6 0.0144593 92.7317 2.3778 1.3099 0.8574 2.7232
7 0.0144646 92.6685 2.4036 1.3298 0.8613 2.7368
8 0.0144668 92.6436 2.4137 1.3372 0.8631 2.7423
9 0.0144677 92.6333 2.4180 1.3401 0.8638 2.7447
10 0.014468 92.6289 2.4199 1.3414 0.8641 2.7458
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VAR system, lag order 1OLS estimates, observations 1880-1894 (T = 15)Log-likelihood = 132.97876Determinant of covariance matrix = 1.3720479e-014AIC = -13.7305BIC = -12.3144HQC = -13.7456Portmanteau test: LB(3) = 81.9904, df = 50 [0.0029]
Table 53: Decomposition of variance for FICCorr (1880-1894)
period std. error FICCorr FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0358729 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0539006 61.3168 6.5341 26.3032 5.5604 0.2854
3 0.0600529 52.2562 6.7336 33.9760 5.0513 1.9829
4 0.0616369 50.1615 7.0033 36.1252 4.8213 1.8887
5 0.0616727 50.1772 7.0052 36.1041 4.8265 1.8871
6 0.061713 50.1460 7.0549 36.0791 4.8299 1.8900
7 0.06173 50.1264 7.0670 36.0747 4.8297 1.9022
8 0.0617415 50.1099 7.0685 36.0915 4.8284 1.9017
9 0.0617419 50.1093 7.0690 36.0912 4.8285 1.9020
10 0.0617423 50.1087 7.0697 36.0909 4.8285 1.9023
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VAR system, lag order 1OLS estimates, observations 1880-1894 (T = 15)Log-likelihood = 126.58076Determinant of covariance matrix = 3.2199682e-014AIC = -12.8774BIC = -11.4613HQC = -12.8925Portmanteau test: LB(3) = 79.6777, df = 50 [0.0048]
Table 54: Decomposition of variance for MAGCont (1880-1894)
period std. error MAGCont d_MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.0232376 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0324533 51.5135 11.2535 24.4708 6.0181 6.7442
3 0.0405108 62.4531 7.9784 18.5331 5.8911 5.1443
4 0.0448786 53.4561 9.2873 27.9269 5.0651 4.2645
5 0.0460142 53.3356 9.9321 27.1660 5.4990 4.0672
6 0.0466579 52.8636 9.7930 27.7222 5.6635 3.9577
7 0.046846 52.6601 9.9761 27.7901 5.6476 3.9261
8 0.0469739 52.6858 9.9241 27.7967 5.6886 3.9048
9 0.0470162 52.5953 9.9639 27.8644 5.6787 3.8978
10 0.0470379 52.6142 9.9562 27.8463 5.6891 3.8942
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VAR system, lag order 1OLS estimates, observations 1880-1894 (T = 15)Log-likelihood = 103.88397Determinant of covariance matrix = 6.6394232e-013AIC = -9.8512BIC = -8.4351HQC = -9.8663Portmanteau test: LB(3) = 67.7535, df = 50 [0.0479]
Table 55: Decomposition of variance for MAGCorr (1880-1894)
period std. error MAGCorr d_MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.0804295 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0903267 80.0049 2.2979 0.0182 2.6334 15.0455
3 0.0914001 79.6420 2.8621 0.2136 2.5804 14.7019
4 0.0922806 78.1648 3.6519 0.2827 2.9678 14.9328
5 0.0924716 78.0413 3.7154 0.2900 3.0592 14.8941
6 0.0924893 78.0363 3.7140 0.2904 3.0602 14.8990
7 0.0924908 78.0339 3.7141 0.2905 3.0601 14.9013
8 0.0924909 78.0338 3.7142 0.2906 3.0601 14.9013
9 0.092491 78.0337 3.7142 0.2906 3.0602 14.9014
10 0.092491 78.0337 3.7142 0.2906 3.0602 14.9014
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VAR system, lag order 1OLS estimates, observations 1880-1894 (T = 15)Log-likelihood = 67.234762Determinant of covariance matrix = 8.7970445e-011AIC = -4.9646BIC = -3.5485HQC = -4.9797Portmanteau test: LB(3) = 86.8994, df = 50 [0.0009]
Table 56: Decomposition of variance for NFCont (1880-1894)
period std. error NFCont d_NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.0342019 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0371083 89.3820 4.7582 2.4384 2.9321 0.4892
3 0.0379967 85.4504 4.7737 4.6439 3.1790 1.9530
4 0.0382577 84.5229 5.0852 4.6485 3.2261 2.5173
5 0.0385157 83.5749 5.5746 5.1424 3.2244 2.4837
6 0.0386021 83.2117 5.6151 5.3595 3.2172 2.5965
7 0.0386267 83.1241 5.6319 5.3527 3.2294 2.6618
8 0.0386528 83.0333 5.6805 5.3980 3.2293 2.6590
9 0.0386629 82.9922 5.6880 5.4239 3.2279 2.6681
10 0.0386653 82.9831 5.6885 5.4236 3.2291 2.6757
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VAR system, lag order 1OLS estimates, observations 1880-1894 (T = 15)Log-likelihood = 58.393796Determinant of covariance matrix = 2.8594409e-010AIC = -3.7858BIC = -2.3697HQC = -3.8009Portmanteau test: LB(3) = 88.097, df = 50 [0.0007]
Table 57: Decomposition of variance for NFCorr (1880-1894)
period std. error NFCorr d_NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.1449 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.155077 95.2345 2.4518 0.9489 1.1278 0.2370
3 0.155729 94.4665 2.8522 1.1832 1.1683 0.3299
4 0.155958 94.2985 2.8764 1.3191 1.1704 0.3355
5 0.155982 94.2917 2.8767 1.3252 1.1701 0.3364
6 0.155986 94.2881 2.8770 1.3274 1.1700 0.3374
7 0.155988 94.2855 2.8770 1.3299 1.1700 0.3375
8 0.155989 94.2853 2.8770 1.3302 1.1700 0.3375
9 0.155989 94.2852 2.8771 1.3302 1.1700 0.3375
10 0.155989 94.2852 2.8771 1.3302 1.1700 0.3375
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Appendix J: Forecast error decompositions of variance (1895–1909)
Full data range: 1815 - 2009 (n = 195)Current sample: 1895 - 1909 (n = 15)
VAR system, lag order 1OLS estimates, observations 1895-1909 (T = 15)Log-likelihood = 139.5307Determinant of covariance matrix = 5.7276044e-015AIC = -14.6041BIC = -13.1880HQC = -14.6192Portmanteau test: LB(3) = 80.6961, df = 50 [0.0039]
Table 58: Decomposition of variance for FICCont (1895 – 1909)
period std. error FICCont FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0167655 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0176758 92.6327 6.3727 0.4218 0.0542 0.5185
3 0.0177802 91.6850 6.5819 0.4183 0.2278 1.0870
4 0.0178189 91.3331 6.5533 0.6961 0.3274 1.0901
5 0.0178326 91.2345 6.5527 0.7010 0.3290 1.1828
6 0.0178386 91.1788 6.5494 0.7527 0.3346 1.1844
7 0.0178399 91.1690 6.5493 0.7529 0.3349 1.1939
8 0.0178405 91.1655 6.5489 0.7567 0.3351 1.1939
9 0.0178406 91.1644 6.5491 0.7567 0.3351 1.1947
10 0.0178407 91.1641 6.5490 0.7570 0.3351 1.1947
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VAR system, lag order 1OLS estimates, observations 1895-1909 (T = 15)Log-likelihood = 117.85473Determinant of covariance matrix = 1.0307191e-013AIC = -11.7140BIC = -10.2979HQC = -11.7290Portmanteau test: LB(3) = 80.883, df = 50 [0.0037]
Table 59: Decomposition of variance for FICCorr (1895-1909)
period std. error FICCorr FIChwvr_init FIC_otoh FIC_INC FIC_FAC
1 0.0483438 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0544823 85.5078 1.0779 0.6468 0.1309 12.6366
3 0.0564964 80.2696 2.3950 2.9951 2.5591 11.7812
4 0.0568586 79.8818 2.5651 3.0045 2.6158 11.9329
5 0.0571038 79.5679 2.5506 3.1327 2.7591 11.9897
6 0.0571452 79.4590 2.5514 3.1800 2.8366 11.9730
7 0.0571574 79.4251 2.5544 3.1848 2.8473 11.9884
8 0.0571653 79.4085 2.5544 3.1918 2.8584 11.9869
9 0.0571668 79.4048 2.5543 3.1929 2.8612 11.9868
10 0.0571674 79.4032 2.5543 3.1933 2.8621 11.9871
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VAR system, lag order 1OLS estimates, observations 1895-1909 (T = 15)Log-likelihood = 116.87537Determinant of covariance matrix = 1.1744944e-013AIC = -11.5834BIC = -10.1673HQC = -11.5985Portmanteau test: LB(3) = 80.283, df = 50 [0.0042]
Table 60: Decomposition of variance for MAGCont (1895-1909)
period std. error MAGCont MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.026604 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0306368 88.9988 0.8384 4.0778 6.0109 0.0741
3 0.0322516 80.3099 1.1401 7.0547 11.3318 0.1635
4 0.032589 78.6678 2.0282 7.7716 11.3687 0.1637
5 0.0327763 78.1514 2.2622 8.0976 11.2750 0.2139
6 0.0329027 77.6906 2.3180 8.3739 11.3955 0.2220
7 0.0329676 77.4002 2.3721 8.5216 11.4842 0.2218
8 0.0329947 77.2857 2.4106 8.5835 11.4972 0.2230
9 0.033009 77.2324 2.4262 8.6153 11.5018 0.2243
10 0.0330172 77.2002 2.4331 8.6338 11.5082 0.2247
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VAR system, lag order 1OLS estimates, observations 1895-1909 (T = 15)Log-likelihood = 100.63357Determinant of covariance matrix = 1.024112e-012AIC = -9.4178BIC = -8.0017HQC = -9.4329Portmanteau test: LB(3) = 71.7772, df = 50 [0.0234]
Table 61: Decomposition of variance for MAGCorr (1895-1909)
period std. error MAGCorr MAGhwvr_init MAG_otoh MAG_INC MAG_FAC
1 0.0607197 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0701132 84.8440 3.3115 3.7618 5.3705 2.7122
3 0.0705332 84.4426 3.3294 3.7204 5.8247 2.6830
4 0.0705808 84.3291 3.3261 3.7237 5.9385 2.6827
5 0.0705824 84.3272 3.3263 3.7236 5.9403 2.6827
6 0.0705834 84.3249 3.3267 3.7247 5.9408 2.6830
7 0.0705838 84.3241 3.3268 3.7248 5.9412 2.6831
8 0.0705838 84.3239 3.3268 3.7249 5.9414 2.6831
9 0.0705838 84.3239 3.3268 3.7249 5.9414 2.6831
10 0.0705838 84.3239 3.3268 3.7249 5.9414 2.6831
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VAR system, lag order 1OLS estimates, observations 1895-1909 (T = 15)Log-likelihood = 78.580589Determinant of covariance matrix = 1.9379657e-011AIC = -6.4774BIC = -5.0613HQC = -6.4925Portmanteau test: LB(3) = 72.6843, df = 50 [0.0197]
Table 62: Decomposition of variance for NFCont (1895-1909)
period std. error NFCont NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.0424595 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.0507808 75.8338 3.7013 13.2559 1.5221 5.6870
3 0.0557743 63.0661 6.8164 12.0795 11.4657 6.5724
4 0.0569007 60.9562 6.8596 13.8078 11.2791 7.0973
5 0.0578043 59.1693 7.6716 13.6179 12.5662 6.9751
6 0.0579947 58.8313 7.7181 13.8561 12.5699 7.0246
7 0.0580948 58.6419 7.8062 13.8322 12.7084 7.0113
8 0.0581186 58.5995 7.8133 13.8618 12.7090 7.0164
9 0.0581305 58.5771 7.8234 13.8589 12.7255 7.0150
10 0.0581334 58.5720 7.8242 13.8626 12.7256 7.0156
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VAR system, lag order 1OLS estimates, observations 1895-1909 (T = 15)Log-likelihood = 64.002139Determinant of covariance matrix = 1.3537074e-010AIC = -4.5336BIC = -3.1175HQC = -4.5487Portmanteau test: LB(3) = 85.2185, df = 50 [0.0014]
Table 63: Decomposition of variance for NFCorr (1895-1909)
period std. error NFCorr NFhwvr_init NF_otoh NF_INC NF_FAC
1 0.0877377 100.0000 0.0000 0.0000 0.0000 0.0000
2 0.106579 68.2280 0.5092 3.0751 4.2807 23.9070
3 0.11097 64.0013 0.5836 6.2254 5.2971 23.8925
4 0.116483 58.8362 1.6281 6.0711 11.5631 21.9015
5 0.117424 58.2845 1.7148 6.4024 11.9038 21.6945
6 0.117531 58.1880 1.7407 6.3986 12.0108 21.6618
7 0.117602 58.1236 1.7579 6.4163 12.0530 21.6492
8 0.117606 58.1206 1.7580 6.4185 12.0550 21.6477
9 0.117611 58.1158 1.7593 6.4189 12.0589 21.6471
10 0.117611 58.1155 1.7593 6.4193 12.0589 21.6471
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Appendix K: Summary tables of FEVDS, 10th horizon, by genreTable 64: Summary of FEVDs, 10th horizon, Fiction genre
Genre/OTC Date range interactingparticle interaction
1. FICCont 1851-1864 FAC 1.89712. FICCont 1851-1864 INC 3.47193. FICCont 1851-1864 otoh 0.78564. FICCont 1851-1864 hwvr_init 2.36065. FICCont 1865-1879 FAC 1.37076. FICCont 1865-1879 INC 3.74577. FICCont 1865-1879 otoh 2.02588. FICCont 1865-1879 hwvr_init 4.32509. FICCont 1880-1894 FAC 2.745810. FICCont 1880-1894 INC 0.864111. FICCont 1880-1894 otoh 1.341412. FICCont 1880-1894 hwvr_init 2.419913. FICCont 1895-1909 FAC 1.194714. FICCont 1895-1909 INC 0.335115. FICCont 1895-1909 otoh 0.757016. FICCont 1895-1909 hwvr_init 6.549017. FICCorr 1851-1864 FAC 9.712118. FICCorr 1851-1864 INC 18.376619. FICCorr 1851-1864 otoh 0.709320. FICCorr 1851-1864 hwvr_init 15.526421. FICCorr 1865-1879 FAC 2.075822. FICCorr 1865-1879 INC 4.586323. FICCorr 1865-1879 otoh 11.114424. FICCorr 1865-1879 hwvr_init 2.763525. FICCorr 1880-1894 FAC 1.902326. FICCorr 1880-1894 INC 4.828527. FICCorr 1880-1894 otoh 36.090928. FICCorr 1880-1894 hwvr_init 7.069729. FICCorr 1895-1909 FAC 11.987130. FICCorr 1895-1909 INC 2.862131. FICCorr 1895-1909 otoh 3.193332. FICCorr 1895-1909 hwvr_init 2.5543
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Table 65: Summary of FEVDs, 10th horizon, Magazine genre
Genre/OTC Date range interactingparticle interaction
1. MAGCont 1851-1864 FAC 4.6082. MAGCont 1851-1864 INC 5.1873. MAGCont 1851-1864 otoh 13.97194. MAGCont* 1851-1864 hwvr_init 16.64785. MAGCont 1865-1879 FAC 7.81116. MAGCont 1865-1879 INC 0.28187. MAGCont 1865-1879 otoh 1.52098. MAGCont 1865-1879 hwvr_init 3.58629. MAGCont 1880-1894 FAC 3.894210. MAGCont 1880-1894 INC 5.689111. MAGCont 1880-1894 otoh 27.846312. MAGCont* 1880-1894 hwvr_init 9.956213. MAGCont 1895-1909 FAC 0.224714. MAGCont 1895-1909 INC 11.508215. MAGCont 1895-1909 otoh 8.633816. MAGCont 1895-1909 hwvr_init 2.433117. MAGCorr 1851-1864 FAC 7.137418. MAGCorr 1851-1864 INC 10.494619. MAGCorr 1851-1864 otoh 5.937320. MAGCorr* 1851-1864 hwvr_init 8.087321. MAGCorr 1865-1879 FAC 7.995122. MAGCorr 1865-1879 INC 7.182923. MAGCorr 1865-1879 otoh 2.507324. MAGCorr 1865-1879 hwvr_init 1.639125. MAGCorr 1880-1894 FAC 14.901426. MAGCorr 1880-1894 INC 3.060227. MAGCorr 1880-1894 otoh 0.290628. MAGCorr* 1880-1894 hwvr_init 3.714229. MAGCorr 1895-1909 FAC 2.683130. MAGCorr 1895-1909 INC 5.941431. MAGCorr 1895-1909 otoh 3.724932. MAGCorr 1895-1909 hwvr_init 3.3268
* Value transformed by first-differencing
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Table 66: Summary of FEVDs, 10th horizon, Nonfiction genre
Genre/OTC Date range interactingparticle interaction
1. NFCont 1851-1864 FAC 2.50852. NFCont 1865-1879 FAC 0.02073. NFCont 1880-1894 FAC 2.67574. NFCont 1895-1909 FAC 7.01565. NFCorr 1851-1864 FAC 5.91476. NFCorr 1865-1879 FAC 2.59217. NFCorr 1880-1894 FAC 0.33758. NFCorr 1895-1909 FAC 21.64719. NFCont 1851-1864 INC 0.805310. NFCont 1865-1879 INC 3.680911. NFCont 1880-1894 INC 3.229112. NFCont 1895-1909 INC 12.725613. NFCorr 1851-1864 INC 9.408314. NFCorr 1865-1879 INC 6.544815. NFCorr 1880-1894 INC 1.1716. NFCorr 1895-1909 INC 12.058917. NFCont 1851-1864 otoh 12.534418. NFCont 1865-1879 otoh 1.694719. NFCont 1880-1894 otoh 5.423620. NFCont 1895-1909 otoh 13.862621. NFCorr 1851-1864 otoh 1.146322. NFCorr 1865-1879 otoh 14.284123. NFCorr 1880-1894 otoh 1.330224. NFCorr 1895-1909 otoh 6.419325. NFCont 1851-1864 hwvr_init 9.301626. NFCont 1865-1879 hwvr_init 1.943527. NFCont* 1880-1894 hwvr_init 5.688528. NFCont 1895-1909 hwvr_init 7.824229. NFCorr 1851-1864 hwvr_init 16.119730. NFCorr 1865-1879 hwvr_init 10.553531. NFCorr* 1880-1894 hwvr_init 2.877132. NFCorr 1895-1909 hwvr_init 1.7593
* Value transformed by first-differencing
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Appendix L: Ranked normalized particle interactions, by genre
Table 67: Normalized particle interactions, Fiction genre, Contrastive OTC
Interacting particle Summed values Z-valuehwvr_init 15.6545 Mean: 9.0474 1.42INC 8.4168 St. Dev.: 4.6387 -0.14FAC 7.2083 -0.40otoh 4.9098 -0.89
Table 68: Normalized particle interactions, Fiction genre, Corrective OTC
Interacting particle Summed values Z-valueotoh 51.1079 Mean: 24.8965 1.48INC 30.6535 St. Dev.: 19.8412 -0.27hwvr_init 27.9139 -0.51FAC 25.6773 -0.70
Table 69: Normalized particle interactions, Magazine genre, Contrastive OTC
Interacting particle Summed values Z-valueotoh 51.9729 Mean: 30.9501 1.36hwvr_init 32.6233 St. Dev.: 15.5037 0.11INC 22.6661 -0.53FAC 16.538 -0.93
Table 70: Normalized particle interactions, Magazine genre, Corrective OTC
Interacting particle Summed values Z-valueFAC 32.717 Mean: 22.1559 1.15INC 26.6791 St. Dev.: 9.2203 0.49hwvr_init 16.7674 -0.58otoh 12.4601 -1.05
Table 71: Normalized particle interactions, Nonfiction genre, Contrastive OTC
Interacting particle Summed values Z-valueotoh 33.5153 Mean: 22.7336 1.22hwvr_init 24.7578 St. Dev.: 8.8718 0.23INC 20.4409 -0.26FAC 12.2205 -1.19
Table 72: Normalized particle interactions, Nonfiction genre, Corrective OTC
Interacting particle Summed values Z-valuehwvr_init 31.3096 Mean: 28.5407 0.75FAC 30.4914 St. Dev.: 3.6797 0.53INC 29.1820 0.17otoh 23.1799 -1.46
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