Is There an “Academic Vocabulary”? · single core vocabulary for academic study. We argue that the different ... included in a general academic vocabulary if it is common to a

Is There an “Academic Vocabulary”?KEN HYLANDUniversity of LondonLondon, England

POLLY TSEHong Kong University of Science and TechnologyHong Kong SAR, China

This article considers the notion of academic vocabulary: the assumptionthat students of English for academic purposes (EAP) should study acore of high frequency words because they are common in an Englishacademic register. We examine the value of the term by using Cox-head’s (2000) Academic Word List (AWL) to explore the distributionof its 570 word families in a corpus of 3.3 million words from a range ofacademic disciplines and genres. The findings suggest that althoughthe AWL covers 10.6% of the corpus, individual lexical items on the listoften occur and behave in different ways across disciplines in terms ofrange, frequency, collocation, and meaning. This result suggests thatthe AWL might not be as general as it was intended to be and, moreimportantly, questions the widely held assumption that students need asingle core vocabulary for academic study. We argue that the differentpractices and discourses of disciplinary communities undermine theusefulness of such lists and recommend that teachers help studentsdevelop a more restricted, discipline-based lexical repertoire.

THE CONCEPT OF AN ACADEMIC VOCABULARY

The idea of an academic vocabulary has a long history in teachingEnglish for academic or specific purposes (EAP and ESP). Variously

known as subtechnical vocabulary (Anderson, 1980; Yang, 1986), semitech-nical vocabulary (Farrell, 1990), or specialized nontechnical lexis (Cohen,Glasman, Rosenbaum-Cohen, Ferrara, & Fine, 1988), the term is used torefer to items which are reasonably frequent in a wide range of academicgenres but are relatively uncommon in other kinds of texts (Coxhead &Nation, 2001). This vocabulary is seen as a key element of essayist literacy(Lillis, 2001) and an academic style of writing and is considered to be moreadvanced (Jordan, 1997) than the core 2,000–3,000 words that typicallymake up around 80% of the words students are likely to encounter inreading English at university (Carter, 1998).

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Many teachers regard helping undergraduates develop control oversuch a specialist vocabulary as an important part of their role, and at-tempts have been made to assemble lists of key terms to guide materialswriters and help students plan their learning more efficiently. Early ESPmaterials, for example, sought to identify and present forms with a highfrequency in scientific and technical writing (e.g., Barber, 1988; Herbert,1965) and considerable effort has been devoted to investigating thevocabulary needed for academic study (e.g., Campion & Elley, 1971;Coxhead, 2000; Nation, 1990). Such research is usually based on theassumption that learners are seeking to build a repertoire of specializedacademic words in addition to their existing basic or general servicevocabulary, and this repertoire building is often seen as the purpose ofdeveloping university vocabulary. Consequently, vocabulary is typicallyseen as falling into three main groups (Nation, 2001):

1. High frequency words such as those included in West’s (1953) GeneralService List (GSL) of the most widely useful 2,000-word families inEnglish, covering about 80% of most texts.

2. An academic vocabulary of words which are reasonably frequent inacademic writing and comprise some 8%–10% of running words inacademic texts.

3. A technical vocabulary which differs by subject area and covers up to5% of texts.

First year undergraduate students are said to find an academic vo-cabulary (2) a particularly challenging aspect of their learning (Li &Pemberton, 1994). This aspect of their learning is challenging because,although technical vocabulary is central to students’ specialized areas,general academic vocabulary serves a largely supportive role and thewords are “not likely to be glossed by the content teacher” (Flowerdew,1993, p. 236). Many of these words also occur too infrequently to allowincidental learning (Worthington & Nation, 1996), encouraging re-searchers and teachers to develop vocabulary lists for directly teachingthese terms.

The notion that some words occur more frequently in academic textsthan in other domains is generally accepted. It also appears to corre-spond with EAP’s distinctive approach to language teaching, based onthe identification of the specific language features and communicativeskills of target groups, and devoted to learners’ particular subject-matterneeds. However, whether it is useful for learners to possess a generalacademic vocabulary is more contentious because it may involve consid-erable learning effort with little return. It is by no means certain thatthere is a single literacy which university students need to acquire toparticipate in academic environments, and we believe that a perspective

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which seeks to identify and teach such a vocabulary fails to engage withcurrent conceptions of literacy and EAP, ignores important differencesin the collocational and semantic behavior of words, and does not cor-respond with the ways language is actually used in academic writing. It is,in other words, an assumption which could seriously mislead students.

In this article we therefore set out to question some of the assump-tions underlying the idea of a general academic vocabulary by analyzingthe distribution of items in the widely used Academic Word List (AWL)(Coxhead, 2000) in a corpus of 3.3 million words from a variety of genresand disciplines.

LISTS OF ACADEMIC VOCABULARY

To identify the most valuable words in academic contexts, a variety ofvocabulary lists have been compiled from corpora, or collections, of aca-demic texts. Corpus studies have shown that the most frequent words inEnglish cover a large percentage of word occurrences in any text. Thetop three words (the, of, to) make up some 10% of uses in the 400-million-word Bank of English corpus, for instance, and the first 100 compriseabout one half of all written and spoken texts (e.g., Hunston, 2002;Kennedy, 1998). Extending the value of such frequency counts to aca-demic uses, researchers have sought to compile lists of frequently occur-ring words found in different disciplines and different kinds of texts(e.g., Coxhead, 2000; Farrell, 1990; Praninskas, 1972; Xue & Nation, 1984).

Common to these lists is the focus on word families, that is, the baseword plus its inflected forms and transparent derivations (Bauer & Na-tion, 1993). This approach overcomes the difficulty of specifying whatcounts as a word by including all closely related affixed forms as well asthe stem’s most frequent, productive, and regular prefixes and affixes. Itis also supported by the view that knowledge of a base word can facilitatethe understanding of its derived forms and evidence which suggests thatmembers of the same word family are stored together in the mentallexicon (Nagy, Anderson, Schommer, Scott, & Stallman, 1989, p. 262;Nation, 2001).

The most recent compilation is the AWL (Coxhead, 2000), whichcontains 570 word families believed to be essential for students pursuinghigher education irrespective of their chosen field of specialization.1 The3,112 individual items in this inventory do not occur in West’s (1953)GSL and were included in the AWL if they met certain frequency and

1 The headwords of families in the AWL together with the items grouped by sublist areavailable from the Massey University (2004) Web site.

IS THERE AN “ACADEMIC VOCABULARY”? 237

range criteria. Words were selected on the basis of occurring at least 100times in an academic corpus of 3.5 million words of varied genres and inat least 15 of the 28 disciplines within the four broad subject groupingsof the corpus: arts, commerce, law, and science (Coxhead, p. 221). Cox-head found that the AWL covered 10% of the words in her corpus andonly 1.4% of a similar-sized corpus of fiction, suggesting that the items aremore relevant for learners with academic purposes. Taken together withitems on West’s GSL, the AWL accounted for 86% of the academic corpus.

There is no doubt that the AWL is an impressive undertaking, repre-senting the most extensive investigation to date into core academic vo-cabulary. By stressing students’ target goals and the need to prioritizeitems in their lexical development, the AWL helps distinguish EAP fromgeneral English and sets an agenda for focused language learning. Butalthough it has become a benchmark for developing teaching materialsfor EAP (e.g., Schmitt & Schmitt, 2005), the list has not been indepen-dently evaluated. It remains unclear how well the AWL can be said torepresent the lexical composition of academic writing in English, and wehave little idea of its coverage in particular disciplines and genres. In fact,a major difficulty of such lists, and not just the AWL, is the assumptionthat a single inventory can represent the vocabulary of academic discourseand so be valuable to all students irrespective of their field of study. Weexplore this hypothesis by examining the frequency, range, preferredmeanings and forms, and the collocational patterns of items in the AWL.

CORPUS AND METHODS

Our study employs both qualitative and quantitative analyses of a me-dium-sized academic corpus organized by discipline, genre, and writerexpertise. Our academic corpus offers a broad cross section of writing inthe disciplines and includes a range of professional and learner textsrepresenting key academic genres across a broad span of disciplines(Table 1). The disciplines are biology, physics, and computer science(sciences); mechanical and electronic engineering (engineering), and so-ciology, business studies, and applied linguistics (social sciences). Withineach of these fields we collected 30 research articles, seven textbookchapters, and 20 academic book reviews in each of seven disciplines; and45 scientific letters in physics and biology. This corpus represents therange of sources students are often asked to read at university and soinclude the kinds of lexical items they will frequently encounter. Inaddition to these professionally written texts we added eight master’stheses, six doctoral dissertations, and eight final-year undergraduateproject theses by L2 students in each of six disciplines to representstudents’ productive uses of vocabulary.

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The academic corpus thus comprises contemporary examples of themain academic discourse genres and includes both long and short texts,published peer-refereed articles, pedagogic texts, and student writing.Unlike Coxhead, whose corpus was opportunistic, including unequalnumbers of texts in each field, a number of 2,000 word text fragmentsfrom the now dated Lancaster-Oslo/Bergen (LOB) Corpus and BrownCorpus as well as the Wellington Corpus, and no examples of studentwriting, we attempted to more systematically represent a range of keygenres in several fields, with equal numbers of entire texts in each genre.Although our procedure resulted in subcorpora of different sizes, wecompensated by comparing item frequencies per 10,000 words.

Having compiled our corpus, we then combined the texts into disci-plines and fields using Corpus Builder (Cobb, 2004) and explored theseusing RANGE, a program developed by Nation (2002) and used to createthe AWL (Coxhead, 2002). This software is preloaded with West’s (1953)GSL of the most frequent 2,000 English words and the AWL, and it showsthe frequency of items from each list in any corpus together with itsrange, or the number of different subcorpora they occurred in. To de-termine the frequency and spread of AWL items, we examined thesefigures for the entire academic corpus, for the three subcorpora of en-gineering, science, and social science, and then for the disciplines withinthese fields. Finally, we ran concordances on selected items to see if theywere used in the same ways and with the same meanings.

RESULTS: IN SEARCH OF AN ACADEMIC VOCABULARY

Overall Frequencies and Distributions

Coxhead and Nation (2001, p. 254) claim that a word should beincluded in a general academic vocabulary if it is common to a range of

TABLE 1Academic Corpus

Sciences Engineering Social Sciences Totals

Research articles 189,800 178,900 633,400 1,002,100Textbooks 106,100 108,000 176,800 390,900Book reviews 31,600 15,900 77,000 124,500Scientific letters 122,000 0 0 122,000Expert writers (subtotal) 449,500 302,800 887,200 1,639,500Master’s theses 139,300 96,200 205,700 441,200doctoral dissertations 191,800 93,900 457,000 742,700Final year project theses 87,100 77,000 305,100 469,200Student writers (subtotal) 418,200 267,100 967,800 1,653,100Overall 867,700 569,900 1,855,000 3,292,600


academic texts and accounts for a substantial number of words in anacademic corpus. In our study, all 570 of the AWL word families oc-curred in the academic corpus, with 541 occurring in all three subcor-pora. As Table 2 shows, the AWL covers an impressive 10.6% of the wordsin the corpus, and together with the 2,000 words of the GSL it providesan accumulative coverage of 85%, representing roughly one unknownword in every seven words of text.

Although the list offers a good overall coverage of our academic cor-pus, we can see that this coverage is not evenly distributed. Students inthe sciences are far less well served because the combined AWL and GSLfailed to account for 22% of the words in our science corpus, meaningthat students would stumble over an unknown item about every fivewords, making the text incomprehensible. This variation may suggestthat writing in the sciences demands a more specialized and technicalvocabulary, but as we discuss later, the fact that all disciplines shapewords for their own uses seriously undermines attempts to describe acore academic vocabulary.

To explore the issue of range further, we examined the frequency ofindividual items in the three subcorpora themselves. Coxhead’s criteriafor uniformity of frequency was 100 occurrences overall with at least 10in each of four fields (Coxhead, 2000, p. 221), but this seemed a re-markably low threshold given the size of her corpus and the fact that theword list contains more than 3,000 individual words. We therefore useda more rigorous and systematic standard, identifying items as frequent ifthey occurred above the mean for all AWL items in the corpus (i.e., 597).Using this measure, only 192 families, or about a third of the AWL items,could be regarded as frequent, with the research terms process, analyze,research, data, and method being the most common.

Interestingly, the top 60 items in our corpus provided a similar cov-erage (3.9%) to Coxhead’s most frequent items, but only 35 items werecommon to both lists. The most frequent 60 families form an importantsublist in Coxhead’s compilation because they represent a coveragemore than twice as numerous as the next most frequent 60 items, about

TABLE 2Coverage of Academic Word List (AWL) in Academic Corpus

Frequencywords AWL items Mean

Coverage %

OverallAWL GSL

Engineering 551,891 61,408 108 11.1 73.3 84.4Social Sciences 1,822,660 200,393 352 11.0 77.0 88.0Sciences 838,926 78,234 137 9.3 69.0 78.3Overall 3,213,477 340,035 597 10.6 74.0 84.7

Note. GSL = General Service List (West, 1953).

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one occurrence every four pages. At the other end of the scale, commence,concurrent, levy, and forthcoming were among 23 families we judged to beextremely infrequent because they occurred less than 60 times in thecorpus (below 10% of the overall mean).

Distributions Across Fields

Looking more closely at the distributions across subcorpora, someitems are frequent overall because of their concentration in one or twosubcorpora, 15 of our top 50 items, for example, had more than 70% oftheir occurrences in one field. Taking the means of individual fields asa benchmark, we found that of the 192 families which were frequentoverall, only 82 were frequent in all three subcorpora and 50 in just one.Nor were the same items the most frequent in all subcorpora. Table 3shows that only analyze and process in the overall most frequent list alsooccurred in the top 10 most frequent families in each subcorpora. Wecan also see here that the AWL items differed considerably in their

TABLE 3Most Frequent Items by Field With Percentages of Families in That Field

Overall (all three fields) Engineering

Family Freq Item % Cum % Family Freq Item % Cum %

Process 4,501 1.3 1.3 Equate 1,418 2.3 2.3Analyze 4,498 1.3 2.6 Process 1,143 1.9 4.2Research 3,841 1.1 3.8 Design 999 1.6 5.8Data 3,789 1.1 4.9 Method 920 1.5 7.3Method 3,214 0.9 5.8 Data 913 1.5 8.8Vary 3,156 0.9 6.8 Analyze 895 1.5 10.2Strategy 3001 0.9 7.6 Function 847 1.4 11.6Culture 2962 0.9 8.5 Require 844 1.4 13.0Function 2909 0.9 9.4 Output 839 1.4 14.4Significant 2742 0.8 10.2 Input 818 1.3 15.7

Sciences Social Sciences

Family Freq Item % Cum % Family Freq Item % Cum %

Data 1395 1.8 1.8 Research 3261 1.6 1.6Method 1271 1.6 3.4 Strategy 2795 1.4 3.0Process 1118 1.4 4.8 Culture 2583 1.3 4.3Analyze 1029 1.3 6.2 Analyze 2574 1.3 5.6Concentrate 865 1.1 7.3 Process 2240 1.1 6.7Require 848 1.1 8.3 Consume 1947 1.0 7.7Function 759 1.0 9.3 Response 1910 1.0 8.6Obtain 750 1.0 10.3 Individual 1894 0.9 9.6Extract 739 0.9 11.2 Participate 1800 0.9 10.5Similar 726 0.9 12.1 Significant 1762 0.9 11.4

Note. Freq = frequency. Cum = cumulative.


spread in each subcorpora, with the top 10 comprising nearly 16% offamilies in engineering and only 11.4% in social sciences.

Distributions are also unequal when we consider the least frequentwords. Using 10% of the mean in each subcorpora as a reference, wefound 78 families to be extremely infrequent in one subcorpora, 63 intwo subcorpora and 6 in all three. In other words, 27% of all the AWLfamilies have a very low occurrence in at least one subcorpora and sohave an extremely low chance of being encountered by students.

Although comparing the words’ frequency of occurrence relative tothe mean helps to determine the relative significance of particular wordsin different subcorpora, variations in the sizes of the subcorpora meanthat this procedure gives us only a general impression of distributions. Amore accurate picture is obtained by norming frequencies to give occur-rences per 10,000 words and then looking at the percentage spread ofeach item across the subcorpora. Because we considered three fields, aneven distribution would be roughly 33% of an item in each subcorpora,but no family met this criteria and more than half of all items occurredmainly in one subcorpora only. Table 4 shows that of the 570 AWLfamilies, 534 (94%) have irregular distributions across the three subcor-pora. Of these, 227 (40% of items) have at least 60% of all instancesconcentrated in just one subcorpora, and 13% have at least 80% in asingle subcorpora. Among the most frequent items, more than 90% of allcases of participate, communicate, output, attitude, conflict, authority, perspec-tive, and simulate occurred in one subcorpora.

Overall, only 36 word families were relatively evenly distributed acrossthe science, engineering, and social science subcorpora, and so mighttherefore be regarded as candidates for an academic word list, albeit avery limited one. Of these, however, only 22 were frequent by our crite-rion of being above the overall mean, and only seven were in the top 60items. Just six families appeared in the top 60 of both Coxhead’s list andour own: analyze, consist, factor, indicate, period, and structure, which to-gether comprised about 0.5% of the corpus. In other words, althoughthe AWL may describe certain high frequency words in the register, the

TABLE 4Concentration of Items in One Field (Adjusting for Text Size)

Number of families

Concentration of items All itemsMost frequent items

(above mean)

40%–59% of occurrences in one field 307 (53.9%) 103 (53.6%)60%–79% of occurrences in one field 154 (27.0%) 48 (25.0%)Over 80% of occurrences in one field 73 (12.8%) 19 (9.9%)Total families with uneven distribution 534 (93.7%) 170 (88.5%)

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distributions in our data suggest that most items have a limited rangeacross subcorpora.

The concentration of items is no less pronounced when we move to afiner level of delicacy and examine how words are distributed withinsubcorpora. Table 5 shows the extent of these concentrations, with 283items in engineering (52% of all families) having more than 65% of allcases in just one discipline, 244 items in the sciences (43%) with morethan 65% in just one discipline, and 128 (22.5%) of items in the socialsciences with more than 65% in one discipline. Overall, only one familyoccurred roughly equally across the three disciplines in the sciences,seven families spread evenly across the social sciences, and 47 across thetwo engineering fields.

Once again then, the patterns suggest a more complex picture oflanguage use in the disciplines than notions of a general academic vo-cabulary allow, pointing to more specialized language uses.

Word Meanings and Uses

There is a further difficulty with compiling a so-called common core ofacademic vocabulary in that not only should it include items that meetfrequency and range criteria, but also items which behave in roughlysimilar ways across disciplines. When reading academic texts, studentsneed to be confident that they are understanding words in the right way,which means a vocabulary list must either avoid items with clearly dif-ferent meanings and dissimilar co-occurrence patterns, or these itemsmust be taught separately rather than as parts of families. We need, then,to be cautious about claiming generality for families whose meaningsand collocational environments may differ across each inflected andderived word form (Oakey, 2003).

Most words have more than one sense and Wang and Nation (2004)have recently examined the potential monosemic bias of the AWL to dis-cover if cases of homography, or unrelated meanings of the same writtenform, misrepresent the composition of word families and so affect the

TABLE 5Concentration of Items in Disciplines (% Adjusted for Corpus Size)

DisciplinesTotal

Families

Total of all items occurring in one discipline

40%–64% 65%–79% Over 80%

Engineering 542 259 (47.8%) 133 (24.5%) 150 (27.7%)Sciences 568 322 (56.7%) 116 (20.5%) 128 (22.5%)Social Sciences 570 409 (71.8%) 74 (13.0%) 54 (9.5%)Overall 570 336 (59.0%) 110 (19.4%) 114 (20.0%)


learning burden for students. Although Wang and Nation found only asmall number of families containing homographs (e.g., major which canmean both important and military rank), they further suggest that wordshave essentially similar meanings across fields. In our corpus, however,different disciplines showed clear preferences for particular meaningsand collocations. This finding has implications for the notion of anacademic vocabulary and attempts to base word lists on it. As brief ex-amples, we might take the two most frequent AWL items in the corpus,process and analyze, both of which occur far more often in academicdiscourse than in other registers.

Despite its high frequency in all three subcorpora, the word process isfar more likely to be encountered as a noun by science and engineeringstudents than by social scientists. This likelihood is the result of the wellknown practice of nominalization, or grammatical metaphor (Halliday,1998), which refers to the way that writers in the sciences regularlytransform experiences into abstractions to create new conceptual ob-jects. Embedding an item such as process into complex abstract nominalgroups produces terms such as• A constant volume combustion process . . .

• the trouble call handling process . . .

• processing dependent saturation junction factors . . .

• the graphical process configuration editor . . .

Though nominalization enables writers in the sciences to give new ob-jects stable names and discuss their properties without further investiga-tion, it does not help novice users unpack specialized meanings from theindividual lexical item. We believe this practice is therefore likely to presentdifficulties to both native and nonnative-English-speaking students.

Similarly, analyze appears to be used differently across fields. In thesocial sciences, it tends to occur more regularly as a noun, while inengineering, students are six times more likely to come across the formanalytical. There are also semantic differences. The word analysis, forinstance, is often associated with particular types of approach, so that itappears in discipline-specific compound nouns such as genre analysis orneutron activation analysis. The verb form also has field-specific meanings.In engineering, it tends to refer to methods of determining the constitu-ent parts or composition of a substance (see Examples 1 and 2), and inthe social sciences it tends to mean simply considering something care-fully (see Examples 3 and 4):

Example 1. The proteins were separated by electrophoresis on 10% poly-acrylamide SDS gels and analyzed by autoradiography. (Physics doctoraldissertation)

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Example 2. We used a variety of methods to analyse fungal spore load,volatiles and toxins. (Biology article)

Example 3. The major objective of this report is to analyze developments inpolitical sociology over the last half century. (Sociology article)

Example 4. Following this, the results were analyzed to determine buyerbehaviour. (Business master’s thesis)

A random analysis of AWL families with potential homographs reveals aconsiderable amount of semantic variation across fields. Table 6 shows themain meanings for selected words with different overall frequencies in theAWL together with their distributions. The first four are from our highfrequency list, with occurrences above the overall mean, and show that evenwhere uses are very frequent, preferred uses still vary widely, with socialscience students far more likely to meet consist as meaning “to stay the same”and science and engineering students very unlikely to come across volumemeaning “a book or journal series” unless they are reading book reviews.With less frequent words the preferred meanings differ dramatically, and wehave to question the value of teaching the secondary meanings at all.

These preferred uses become more apparent when we consider patternsat the disciplinary level. In Table 7, for instance, we can see minimal uses ofissue to mean “flow out” in all disciplines but business, an overwhelmingpreference for attribute to mean a “feature” in business, sociology, and com-puter science and as the verb “to accredit” in applied linguistics, biology, andphysics. Similar disciplinary reversals arise in the preferred uses of volume,credit, abstract, and offset among these examples, and no doubt there are morein the corpus that we have not analyzed.

In addition, all disciplines adapt words to their own ends, displayingconsiderable creativity in both shaping words and combining them with

TABLE 6Distribution of Meanings of Selected Academic Word List Word Families Across Fields (%)

Family Meaning Science Engineering Social Science

Consist stay the same 34 15 55(rank 41) made up of 66 75 45Issue flow out 7 6 18(46) topic 93 94 82Attribute feature 83 35 60(93) ascribe to 17 65 40Volume book 1 7 50(148) quantity 99 93 50Generation growth stage 2 2 36(245) create 98 98 64Credit acknowledge 0 60 52(320) payment 100 40 48Abstract précis/extract 76 100 13(461) theoretical 14 0 87offset counter 0 14 100(547) out of line 100 86 0


others to convey specific, theory-laden meanings associated with disciplinarymodels and concepts. Thus, only texts in business studies contain the formissuer, for example, and 56% of instances of convert are related to “convert-ible security” (42% of all cases) and “convertible bond” (14% of all cases).

Also creating difficulties for learners is the fact that words take on addi-tional meanings as a result of their regular co-occurrence with other items(e.g., Arnaud & Bejoint, 1992), so that the term value in computer science,for instance, is often found as value stream (21% of all cases) and multiple-value attribute mapping (7% of all cases). Even high frequency items such asstrategy have preferred associations with marketing strategy forming 11% of allcases in business, learning strategy making up 9% of cases in applied linguis-tics, and coping strategy constituting 31% of cases in sociology. Nor are wordchoices themselves simply arbitrary selections from an equally appropriatepool of semantically equivalent candidates. Frequency patterns reveal cleardisciplinary preferences as routine uses take on the constancy of convention.The word phase, for example, is favored in biology, where it co-occurs with alimited range of items such as stationary and mobile; in mechanical engineer-ing, stage is preferred, and in applied linguistics, the preferred term is period.

In sum, these different word choices, collocates, and fixed phrases colourthe everyday uses of words with more particular discipline-specific meanings,reflecting how writers need to represent themselves and their ideas througha locally appropriate theoretical and methodological framework. These pat-terns reinforce the view that particular lexical bundles, or strings of wordswhich “commonly go together in natural discourse” (Biber, Johansson,Leech, Conrad, & Finegan, 1999, p. 990), also contribute to meaning-making in academic contexts. Such stable word combinations are an impor-

TABLE 7Distribution of Meanings of Selected Academic Word List Word Families Across

Disciplines (%)

Families Meanings AL BS Soc EE ME Bio CS Phy

Consist stay the same 54 74 63 30 20 38 64 35(41) made up of 46 26 37 70 80 62 36 65Issue flow out 3 28 4 14 0 0 0 0(46) topic 97 72 96 86 100 100 100 100Attribute feature 21 90 71 36 56 12 99 0(93) ascribe to 79 10 29 64 44 88 1 100Volume book 96 1 80 5 41 4 0 14(148) quantity 4 99 20 95 59 96 100 86Generation growth stage 38 18 71 1 6 3 8 1(245) create 62 82 29 99 94 97 92 99Credit acknowledge 65 9 71 100 44 0 0 0(320) payment 35 91 29 0 66 0 100 0Abstract précis/extract 12 28 20 75 80 100 92 0(461) theoretical 88 72 80 25 20 0 8 100offset counter 100 100 100 4 0 25 0 0(547) out of line 0 0 0 96 100 75 100 100

Note. AL = applied linguistics, BS = business sciences, Soc = sociology, EE = electronic engi-neering, ME = mechanical engineering, Bio = biology, CS = computer science, Phy = physics.

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tant part of a discipline’s discoursal resources but enormously complicatethe business of constructing general word lists. By breaking into single wordsitems which may be better learnt as wholes, vocabulary lists simultaneouslymisrepresent discipline-specific meanings and mislead students.

DISCUSSION

Despite the pedagogic attraction of a universal academic vocabulary,there appear to be good reasons to approach the concept with caution.A growing body of research suggests that the discourses of the academy donot form an undifferentiated, unitary mass, as might be inferred from suchgeneral lists as the AWL, but constitute a variety of subject-specific literacies(e.g., Hyland, 2000, 2002). Such ideas as communicative competence inapplied linguistics, situated learning in education, and social constructivismin the social sciences have contributed to a view which places the notionof community at the heart of writing. Each subject discipline represents away of making sense of human experience that has evolved over genera-tions, and each is dependent on its own particular rhetorical practices.

Words are often associated with different meanings and uses acrossregisters (e.g., Biber et al., 1999) and we tend to find similar variationsin the practices in different disciplines. As Trimble (1985) cautions,academic vocabulary can take on extended meanings in technical con-texts, and in different disciplinary environments words may have quitedifferent meanings. We noted earlier, for instance, that the AWL familieswere underrepresented in the sciences, suggesting the need to recognisea distinct scientific vocabulary. The resources which have developed inthe sciences for construing reality as a world of logical relations andabstract entities are far removed from our routine ways of describing theworld and so represent a more precise disciplinary lexical arsenal. But allfields, not only the sciences, draw on a specialized lexis.

All academic representations shape and manipulate language for dis-ciplinary purposes, often refashioning everyday terms so that words takeon more specific meanings. In fact, it would be surprising if specialisedmeanings had not evolved to discuss field-specific topics more precisely.Persuasion in academic writing involves using language to relate indi-vidual beliefs to shared experience, fitting observations and data intopatterns which are meaningful to disciplinary insiders (Bazerman, 1994;Geertz, 1988). Writers must encode ideas and frame arguments in waysthat their particular audience will find most convincing, drawing onconventional ways of producing agreement between members and fre-quently moulding everyday words to the distinctive meanings of thedisciplines (e.g., Hyland, 2000; Myers, 1990). In other words, differentviews of knowledge, different research practices, and different ways of


seeing the world are associated with different forms of argument, pre-ferred forms of expression, and, most relevantly, specialised uses of lexis.

Corpus evidence shows that words do not occur randomly in languageuse and that choices are governed by both rule-based systems of catego-ries (e.g., Sinclair, 1991) and community-based conventional practices.The findings of this study show that these practices do not just operateat the level of register, as assumed in the notion of academic vocabulary,but indicate greater specificity which undermine claims that there is ageneral vocabulary of value to all students preparing for, or engaged in,university study. We have found that various parameters of variationsupport this assertion, and analysis of our corpus using the most recentlist reveals uneven word frequencies, restricted item range, disciplinarypreferences for particular items over semantic equivalents, and addi-tional meanings lent to items by disciplinary convention and associationsin lexical bundles.

Many items are considerably underrepresented in particular fields ordisciplines, and this clustering in one field suggests a considerable de-gree of disciplinary specificity in their use. The AWL seems to be mostuseful to students in computer sciences, where 16% of the words arecovered by the list, and least useful to students in biology, with only 6.2%coverage. Coxhead acknowledged a bias toward the commerce subcor-pus in her study but investigated no further. It seems likely, however, thatthis bias resulted from the inclusion of disciplines which shared greatersimilarities in her commerce corpus (e.g., accounting, economics, andfinance), and dissimilar disciplines in arts (e.g., psychology, history, andlinguistics) and sciences (e.g., geography, mathematics, and biology).This procedure seems to have produced remarkably high frequencies ofwords in the AWL common to finance-oriented disciplines, such as con-sume, corporate, invest, partner, purchase, and finance. These words all occuroverwhelmingly in business studies and, because it tends to develop busi-ness-related applications, in the computer studies texts in our corpus.

Similarly, a law subcorpus comprises a full quarter of Coxhead’s da-tabase, and a glance at the AWL shows a considerable number of law-related headwords (legal, legislate, regulate, compensate, etc.) which areunlikely to figure significantly in the texts of most undergraduates. Weare also concerned that the AWL corpus is partly made up of 2,000-wordtext fragments rather than whole texts and that some of these, namelythe Brown and LOB Corpora, date back almost 30 years. In short, webelieve that the selection of Coxhead’s corpus may have skewed the AWLand squeezed other potential candidates out of the corpus altogether.

These findings therefore serve to undermine the value of relying ondecontextualized lists of vocabulary as a source of generally available andequally valid items for student writers across the disciplines. Within eachdiscipline or course, students need to acquire the specialized discourse

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competencies that will allow them to succeed in their studies and par-ticipate as group members. This means seeing literacy as something wedo, an activity “located in the interactions between people” (Barton andHamilton, 1998, p. 3), rather than as a thing “distanced from bothteacher and learner and imposing on them external rules and require-ments” (Street, 1995, p. 114). Because literacy is a practice integral toparticular social and institutional contexts, we are forced to acknowledgethat lists which claim to represent an academic vocabulary are likely tohave a limited usefulness. The words they contain are unlikely to be ofequal value to all students, and many words will be of almost no use tothem at all. As teachers, we have to recognize that students in differentfields will require different ways of using language and so we cannotdepend on a list of academic vocabulary.

We are therefore unable to support the division between academic andtechnical vocabulary assumed by list compilers. The fact is that althoughsome words appear to be more generally used in the register, rangingacross several disciplines, these items are not used in the same way anddo not mean exactly the same thing in different disciplinary contexts.Instead, it might be more accurate to regard academic vocabulary as acline of technically loaded or specialized words ranging from termswhich are only used in a particular discipline to those which share somefeatures of meaning and use with words in other fields. The generalpoint is, however, that we need to identify students’ target languageneeds as clearly as possible and to address these needs as well as we can,and part of this work will involve introducing, making salient, and prac-ticing the specialized vocabulary of their fields or disciplines.

Nor do we find reasons to be sanguine about the unexamined viewthat students first acquire a multipurpose general service vocabularywhich they only then top up with an academic repertoire. Clearly, manystudents learn English for purposes other than higher education andmay not encounter large numbers of academic words in their studies.But often students do not follow this kind of trajectory and, instead,build their vocabularies less systematically, as they come across wordsfrom a variety of sources. Second language acquisition research indicatesthat students are likely to acquire items as they need them rather than ina taught sequence, and students will meet many of these academic wordsbefore gaining control of a general service vocabulary. This sort of ac-quisition is particularly likely given the fact that the AWL assumes knowl-edge of the words in the rather archaic GSL, which is now more than 50years old and does not reflect current usage. It is not hard to imagine, forexample, a student encountering so-called academic items (from theAWL) such as available, percent, achieve, similar, and assist before GSLitems such as headdress, shilling, redden, cultivator, and beak.

In sum, because academic knowledge is embedded in processes of


argument and consensus-making, it will always be particular to specificdisciplines and the socially agreed on ways of discussing problems inthose disciplines. The fact that writing actually helps to create disciplines,rather than being just another aspect of what goes on in them, is aserious challenge to identifying uniformities in academic language usethat apply in the same ways to all disciplines. If this view is correct, thenit is also a serious challenge for students crossing disciplinary boundariesin modular and joint degree courses. As Bhatia (2002) observes: “Stu-dents interacting with different disciplines need to develop communica-tion skills that may not be an extension of general literacy to handleacademic discourse, but a range of literacies to handle disciplinary varia-tion in academic discourse” (p. 27). It is difficult to see how studentsequipped with a general academic vocabulary are in a position to makethese transitions.

Although this article may tend toward the negative, we have not setout to discredit the AWL. On the contrary, we feel it offers a usefulcharacterization of register-level vocabulary choices which may providelearners with a basis for challenging stereotypes or examining specificpractices in their own fields. We also agree with the pedagogical prin-ciples that lay behind it: that teachers should seek to teach the mostrelevant and useful vocabulary to their students and that corpus analysesare the best way of ascertaining which vocabulary to teach (Coxhead,2002). However, the evidence from this study urges caution in seeing thesurfaces of texts as assemblies of discrete words with similar uses andmeanings. By considering context, cotext, and use, academic vocabularybecomes a chimera. It gives a misleading impression of uniform practicesand offers an inadequate foundation for understanding disciplinary con-ventions or developing academic writing skills.

CONCLUSIONS AND IMPLICATIONS

Designing EAP materials is a complex task and, by arguing for morespecific approaches to vocabulary teaching, it may seem that we aremaking it even harder. There are, however, important theoretical andpedagogic issues at stake which make it important to examine the as-sumptions that inform the idea of a general academic vocabulary. Mod-ern conceptions of EAP, and language teaching more generally, stressthe role of communication rather than language and the processes bywhich texts are created and used as much as they stress the texts them-selves. Thus, teaching looks beyond lists of common core features andthe autonomous views of literacy that such lists assume, recognizing thatcontextual factors are crucial to language choices because we commu-nicate as members of social groups.

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Vocabulary lists, such as the AWL, may provide some guidelines forteaching purposes, but if we find that individual items occur and behavein dissimilar ways in different disciplines then we are forced to acknowl-edge the importance of contextual environments which reflect differentdisciplinary practices and norms. Obviously vocabulary is more than in-dividual words acting separately in a discourse, and compilers of vocabu-lary lists rightly insist that items should not be learnt out of context (e.g.,Coxhead, 2000, 2002; Coxhead & Nation, 2001; Nation, 2001). Acquisi-tion clearly needs to be part of a well-planned and sequenced program,with a mix of explicit teaching and incidental learning, a range of ac-tivities which focus on elaboration and consolidation, and sufficient in-formation about contexts and definitions. We would argue, however,that the most appropriate starting point for such a program, offering thebest return for learning effort, is the student’s specific target context.

Like the AWL itself, corpus-informed lists and concordances can beused to help establish vocabulary learning goals for EAP courses, designrelevant teaching materials, and generally target instruction more care-fully. We believe it is important, however, that these lists and concor-dances are derived from the genres students will need to write and thetexts they will need to read. Learners should be encouraged, for ex-ample, to notice these high frequency items and multiword unitsthrough repeated exposure and through temporary decontextualisationactivities such as matching and item identification activities. Conscious-ness-raising tasks which offer opportunities to retrieve, use, and manipu-late items can be productive, as can activities which require learners toproduce the items in their extended writing. Students can, in fact, beencouraged to explore specialised corpora themselves and to try to iden-tify meanings inductively from repeated examples or to familiarise them-selves with common collocates.

In sum, although the generic label academic vocabulary may be a con-venient shorthand for describing a general variety, it conceals a wealth ofdiscursive variability which can misrepresent academic literacy as a uni-form practice and mislead learners into believing that there is a singlecollection of words which they can learn and transfer across fields. As wehave learnt more about the different contexts in which students findthemselves at university, the complexity of the particular communicativedemands their studies placed on them has become increasingly clear. Wehave began to see that many language features, including vocabulary, arespecific to particular disciplines, and that the best way to prepare stu-dents for their studies is not to search for overarching, universally ap-propriate teaching items, but to provide them with an understanding ofthe features of the discourses they will encounter in their particularcourses.


THE AUTHORS

Ken Hyland is Professor of Education and Head of the Centre for Academic andProfessional Literacies at the Institute of Education, University of London, London,England. He has taught in numerous countries and published more than 120 articlesand 11 books on writing and applied linguistics.

Polly Tse is an instructor at the Hong Kong University of Science & Technology,Hong Kong SAR, China, where she teaches English for specific purposes to businessand science students. Her research interests include academic writing, interaction intext, and systemic functional linguistics.

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