D.Gile TipsPhD 1

TIPS FOR PhD RESEARCH IN TRANSLATION AND INTERPRETING

Daniel Giledaniel.gile@yahoo.com www.cirinandgile.com

D.Gile TipsPhD 2

APPROACHES IN THESES AND DISSERTATIONS – A REMINDER

1. CSA (The traditional “scientific method”)

2. HSA Other approaches, essentially non-empirical, mostly in the

Human Sciences

3. PROFESSIONAL EXPERIENCE, REFLECTION, GENERALIZATION (PRG)

Not really academic, but share the same publication media as academic approaches

Will not be addressed here

4. TECHNOLOGICAL PROJECTS Developing Software, Machines, etc. Will not be addressed here

D.Gile TipsPhD 3

CSA – ESSENTIALLY EMPIRICAL

Strongly data-oriented – but not necessarily quantitative The data are used to develop/test theories

Theories are conceptual constructs used as tools to represent reality until further evidence leads to better tools

(which have better explanatory/predictive power)

Started in the natural sciences Was adopted later in other disciplines

In TS, found in research on: - translation processes - Translation quality - Linguistic aspects of translation - Translation universals

D.Gile TipsPhD 4

HSA (1)

Mostly conceptual analysis

Often based on evidence… (but not locked-in to observable, explicitly reported

evidence)

Often with personal, subjective interpretation of statements and phenomena without systematic attempts to test them empirically

Most of the progress is achieved through the analysis of ideas and through debates in which existing theories are discussed

D.Gile TipsPhD 5

HSA (2)

Found in many - Philosophical - Literary - Sociological, political, ideological studies of translation

Often incompatible with CSA because of:

No attempt to be objective, sometimes deliberately subjective

Links to factual evidence flimsy

Can be evaluative without evidence as a backing for judgment, prescriptive

D.Gile TipsPhD 6

REQUIREMENTS FROM PhD

1. COMPLY WITH ACADEMIC NORMSWith respect to fundamental intellectual rules and with

respect to writing and social norms

2. INNOVATEWith new facts, ideas and/or research methods

In most academic settings, you will have to choose between CSA and HSA

In most cases, it is easier to innovate in CSA than in HSA

Because it is easier to find an under-explored part of reality than an under-analyzed conceptual system where

you can really innovate

D.Gile TipsPhD 7

TOPIC SELECTION: THE MOST DIFFICULT PART OF THE RESEARCH PROJECT?

- Long hesitations before deciding- Stuck in the middle because of unforeseen problems- Deadline problems

TWO MAJORS REASONS FOR DIFFICULTIES- Poor topic selection/definition (Too ambitious, not feasible, not defined clearly enough)- Poor planning

PREVENTION IS POSSIBLE

D.Gile TipsPhD 8

SPECIFIC OBJECTIVES

Each project needs specific objectives, such as finding an answer/beginning of an answer to questions

- What can I find out about X?- Can I detect regularities in Y?

- What happens if..?- Is Z true? (testing a theory/hypothesis)

- What can I add to theory T?

In all these cases, some innovation is expected from the study

But not major innovation! Check published work and see for yourself

D.Gile TipsPhD 9

HOW TO START?

Ask yourself:- What areas of translation/interpreting am I interested in?

- What issues in these areas am I interested in?

- What unresolved issues have I identified in them?

Read the literature systematically and ask yourself:

- Where could I contribute something new? (new facts, new ideas, new methods)?- How could I do that? (a general idea of what you would do concretely)- Can I really? (knowledge, knowhow, materials, subjects, time)

D.Gile TipsPhD 10

Doctoral projects…dreams and reality (1)

Doctoral work requires sustained effort

Part of it is pleasure, but much effort is tedious

Expect:

- Doubts- Difficulties

- Much repetititve data collection, uninteresting computations, rewriting, proof-reading…

- Crises- Interference from/with other activities

but Also pleasure as you work and progress

D.Gile TipsPhD 11

Doctoral projects…dreams and reality (2)

Don’t expect to make major discoveries

You may, if you are very lucky

But most of the time, you will not(because of variability, of limited resources, of complexities

you discover as you go along…)

Expect your innovative contribution to be modest

To avoid overly excessive objectives which might results in failure

To avoid being disappointed by your own findingsAnd by other people’s reactions to your work

D.Gile TipsPhD 12

THREE (LEGITIMATE) CASES

Project for pleasureNo restrictions… but remember to plan stng feasible

Project to meet academic requirements(Tenure, promotion…)

Select topic for minimum effort to meet requirements(you may well find the work pleasurable and want to do

more)

Project with a specific outcome in your mindSeek maximum efficiency

But be reasonable in your ambitionsRemember that high variability is a big problem

And so is access to a large enough representative sample

D.Gile TipsPhD 13

PRACTICAL STRATEGIES

Practical planning - is important

- is part of the topic selection process

Remember thatyour initial topic / objectives / research question

may not be the final ones

Keep your mind open to:

A narrower topic than initially plannedAlternative routes

Changes in the direction in which you will progress

D.Gile TipsPhD 14

PLANNING AND FEASIBILITY (1)

Check feasibility when planning

Small scale study OK, often advisable

If empirical, simple methodology often advisable(Unless the research questions warrant advanced methods

and you have the necessary skills and/or help)

Choose a specific topic within a subject area and start planning, but

Don’t fall in love with this topicYou may find it wiser to change course if feasibility is

uncertain or if problems crops up

D.Gile TipsPhD 15

PLANNING AND FEASIBILITY (2)

Be realistic

Do not try to solve a fundamental problem:

Try to contribute something towards a solution

Do not be disappointed

if you do not find clear-cut results

The absence of clear-cut results may be just as useful(examples: directionality issue, training in simultaneous

with or w/o previous training in consecutive,Requirement for long stay in country of B language prior to

enrollment…)

D.Gile TipsPhD 16

PLANNING CHECKLIST

TIMETOTAL TIME TO COMPLETION

REGULAR TIME TO WORK

METHOD AND RESOURCESCHOOSE METHOD CONSIDERING AVAILABILITY OF

RESOURCES

BASELINE EXPERTISEDO YOU HAVE IT ?

DO YOU HAVE TIME TO ACQUIRE IT?CAN YOU GET OUTSIDE HELP?

AVAILABLE SAMPLE(SUBJECTS, MATERIALS, EQUIPMENT, TEXTS…)

- SIZE?- REPRESENTATIVE OF WHAT?

D.Gile TipsPhD 17

PROTOTYPICAL HSA & CSA – CSA (1)

CSA: Around dataStart with question or hypothesis

For which you wish to find an answer with data

When checking feasibility, think of data- Access to what data?

- How will you process it?

The whole research process will be directed towards finding an answer to your question

Every step will be based on the data and on logic

D.Gile TipsPhD 18

PROTOTYPICAL CSA & HSA – CSA (2)

Objective of project:- Explore an unknown part or reality

(Court interpreting in Malaysia, Specific problems in Signed Language Interpreting in educational

settings)- Test a hypothesis

(Do interpreters work close to saturation? Do translators work better from A language? Into A

language?)- Develop a research method

(For instance, for measuring translation quality)

D.Gile TipsPhD 19

PROTOTYPICAL CSA & HSA - HSA

Starts with general ideas and/or questions about the nature of something

Reading and reflection, analysis of other authors’ ideas(in CSA, much analysis of other authors’ findings and

methods)

Access to data for observation/measurement is irrelevant

The whole research process will be directed towards finding arguments in favor of or against certain ideas or theories

Progression based on ideas, not on dataData are not brought in to justify every statement.

but may be brought in by your contradictors, so be aware of them nevertheless.

D.Gile TipsPhD 20

EXAMPLE – DIRECTIONALITY (1)

Am interested in the issue of directionalityHave read the relevant literature, which is mostly

prescriptiveAm aware in particular of of Nike Pokorn’s doctoral

dissertation, which tackles directionality through identification of the translator’s A language.

Think this is not sufficient, and think I may be able to contribute by doing a direct comparison of work into A

and into B

How to go about it?

D.Gile TipsPhD 21

EXAMPLE – DIRECTIONALITY (2)

First question: naturalistic or experimental?

If naturalistic, how to go about it?Access to translations into A and into B?

If only literary OK?If not, how access?

Through translation company?Check the possibilities

Through translation department in organization?Check the possibilities

Access to how many people into A?Access to how many people into B?

What will I check?Language quality? Fidelity?

D.Gile TipsPhD 22

EXAMPLE – DIRECTIONALITY (3)

How will I check language/fidelity?

If linguistic quality, raters?Variability? Comparability?

How many texts?How will I process the data?

If fidelity, how?Propositional? Word for word?

Selected words?

How much time do I need for the work?

D.Gile TipsPhD 23

EXAMPLE – DIRECTIONALITY (4)

If experimental:Is one experiment enough for a PhD?

If not, how much? What design?

How many people, how many texts, what order?(Same texts need to be translated into A and into B by

sufficient translators to provide data with a chance to overcome variability barriers)

What texts?How do I get participants to translate them?

How do I ensure the ecological validity of the design while controlling relevant parameters?

D.Gile TipsPhD 24

EXAMPLE – DIRECTIONALITY (5)

How much time do I need:

For the selection of texts, for the selection of participants,for piloting, for data analysis?

(Count several months for writing after analysis of data completed)

Do I have the required knowledge in statistics to do the hypothesis-testing if I choose to do so? Or access to a

statistician?Or do I choose another type of approach, more qualitative?

If so, what can I expect from such a qualitative investigation?

If so, how?Selection of participants, time….?

D.Gile TipsPhD 25

EXAMPLE – DIRECTIONALITY (6)

Or do I choose a technological approach, based on indicators such as gaze analysis, translog, a combination

of both?

If so, what would I gain?

Is the equipment available?

Do I know how to use it?

I will have to read reports of studies conducted with Translog and gaze analyzers and talk to researchers who

did such work

D.Gile TipsPhD 26

INTERDISCIPLINARY WORK ISSUES

Insufficient baseline knowledge and knowhow on both sides can be problematic

Lack of understanding of TS from non-TS

Cannot necessarily import methods without adapting them

Possible differences in the interpretation of data

Non-TS colleagues/supervisors will not necessarily check your work reliably –

And may ask you to comply with requirements which you consider irrelevant or damaging to the potential added

value of your work(such as control of variables which challenges ecological

validity and reduces potential sample size)

D.Gile TipsPhD 27

SUPERVISOR (1)

IMPORTANT “RESOURCE”Because

(Potential)- Knowledge- Experience

- Support

- AVAILABLE LOCALLY ?(Sometimes institutional requirement)

- KNOWS YOUR FIELD AND SUB-FIELD?

- HAS THE RELEVANT KNOWHOW?

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SUPERVISOR (2)

- AVAILABLE?(Not too many other students/activities?)

DO YOUR PERSONALITIES “FIT”?

FORMAL CO-SUPERVISOR

INFORMAL CO-SUPERVISOR(S)/ADVISORS

D.Gile TipsPhD 29

WORKING WITH YOUR SUPERVISOR (1)

DOES SUPERVISOR SUGGEST A TOPIC?(IF SO, MOTIVATION AND COMPETENCE)

IF NOT, TAKE THE INITIATIVE

THE WORK IS YOURSSUPERVISORS WILL NOT DO IT FOR YOU

SUGGEST TOPIC TO SUPERVISORASK FOR REACTIONS AND ADVICE

PREPARE WRITTEN DESCRIPTION OF METHOD(CLEAR AND CONCISE)ASK FOR REACTION

D.Gile TipsPhD 30

WORKING WITH YOUR SUPERVISOR (2)

Report to your supervisor regularly(Unless s/he tells you not to)

Make sure to report and consult if stuck!

When you do, don’t ask cosmological questionsPrepare specific questions

Make sure to have readily availableany explanation which might be required to help the

supervisor understand your problem

D.Gile TipsPhD 31

WORKING WITH YOUR SUPERVISOR (3)

Remember that while you are immersed in your research,your supervisor has other students and other activities

Your work is not as important for him/her as for you

S/he may well have forgotten what you have done last time your reported on your work

Don’t embarrass him/her, help him/her help youBy reminding him/her briefly of your specific topic,

research questions, present status… before putting a question that requires this background information

D.Gile TipsPhD 32

WORKING WITH YOUR SUPERVISOR (4)

Some supervisors like face-to-face meetings

Some prefer email exchangesYou may miss less when it is written than when it is spoken,

You will have more time to think about it… and can present it as evidence if required

Don’t expect your supervisor to print out 10 pages or more of text you will send him/her

If you have a long text to send, send a hard copy(unless the supervisor asks you for an electronic one)

D.Gile TipsPhD 33

SCIENTIFIC NORMS AND PARADIGMS

There are many paradigms in the field

Theoretical work OK, empirical work OK

Samples OK, case study can be OK

Inferential statistics are not an necessarily an essential part of empirical research

Experimental OK, naturalistic OK

Don’t let yourself be trapped by evaluative claims on paradigms

Look at precedents both in TS and in established disciplines

Accept local norms and constraints, but feel free within their boundaries