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D.Gile TipsPhD1 TIPS FOR PhD RESEARCH IN TRANSLATION AND INTERPRETING Daniel Gile

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Presentation on theme: "D.Gile TipsPhD1 TIPS FOR PhD RESEARCH IN TRANSLATION AND INTERPRETING Daniel Gile"— Presentation transcript:

1 D.Gile TipsPhD1 TIPS FOR PhD RESEARCH IN TRANSLATION AND INTERPRETING Daniel Gile

2 D.Gile TipsPhD2 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

3 D.Gile TipsPhD3 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

4 D.Gile TipsPhD4 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

5 D.Gile TipsPhD5 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

6 D.Gile TipsPhD6 REQUIREMENTS FROM PhD 1. COMPLY WITH ACADEMIC NORMS With respect to fundamental intellectual rules and with respect to writing and social norms 2. INNOVATE With 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

7 D.Gile TipsPhD7 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

8 D.Gile TipsPhD8 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

9 D.Gile TipsPhD9 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)

10 D.Gile TipsPhD10 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

11 D.Gile TipsPhD11 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 findings And by other people’s reactions to your work

12 D.Gile TipsPhD12 THREE (LEGITIMATE) CASES Project for pleasure No 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 mind Seek maximum efficiency But be reasonable in your ambitions Remember that high variability is a big problem And so is access to a large enough representative sample

13 D.Gile TipsPhD13 PRACTICAL STRATEGIES Practical planning - is important - is part of the topic selection process Remember that your initial topic / objectives / research question may not be the final ones Keep your mind open to: A narrower topic than initially planned Alternative routes Changes in the direction in which you will progress

14 D.Gile TipsPhD14 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 topic You may find it wiser to change course if feasibility is uncertain or if problems crops up

15 D.Gile TipsPhD15 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… )

16 D.Gile TipsPhD16 PLANNING CHECKLIST TIME TOTAL TIME TO COMPLETION REGULAR TIME TO WORK METHOD AND RESOURCES CHOOSE METHOD CONSIDERING AVAILABILITY OF RESOURCES BASELINE EXPERTISE DO 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?

17 D.Gile TipsPhD17 PROTOTYPICAL HSA & CSA – CSA (1) CSA: Around data Start 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

18 D.Gile TipsPhD18 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)

19 D.Gile TipsPhD19 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 data Data are not brought in to justify every statement. but may be brought in by your contradictors, so be aware of them nevertheless.

20 D.Gile TipsPhD20 EXAMPLE – DIRECTIONALITY (1) Am interested in the issue of directionality Have read the relevant literature, which is mostly prescriptive Am 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?

21 D.Gile TipsPhD21 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?

22 D.Gile TipsPhD22 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?

23 D.Gile TipsPhD23 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?

24 D.Gile TipsPhD24 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….?

25 D.Gile TipsPhD25 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

26 D.Gile TipsPhD26 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)

27 D.Gile TipsPhD27 SUPERVISOR (1) IMPORTANT “RESOURCE” Because (Potential) - Knowledge - Experience - Support - AVAILABLE LOCALLY ? (Sometimes institutional requirement) - KNOWS YOUR FIELD AND SUB-FIELD? - HAS THE RELEVANT KNOWHOW?

28 D.Gile TipsPhD28 SUPERVISOR (2) - AVAILABLE? (Not too many other students/activities?) DO YOUR PERSONALITIES “FIT”? FORMAL CO-SUPERVISOR INFORMAL CO-SUPERVISOR(S)/ADVISORS

29 D.Gile TipsPhD29 WORKING WITH YOUR SUPERVISOR (1) DOES SUPERVISOR SUGGEST A TOPIC? (IF SO, MOTIVATION AND COMPETENCE) IF NOT, TAKE THE INITIATIVE THE WORK IS YOURS SUPERVISORS WILL NOT DO IT FOR YOU SUGGEST TOPIC TO SUPERVISOR ASK FOR REACTIONS AND ADVICE PREPARE WRITTEN DESCRIPTION OF METHOD (CLEAR AND CONCISE) ASK FOR REACTION

30 D.Gile TipsPhD30 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 questions Prepare specific questions Make sure to have readily available any explanation which might be required to help the supervisor understand your problem

31 D.Gile TipsPhD31 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 you By reminding him/her briefly of your specific topic, research questions, present status… before putting a question that requires this background information

32 D.Gile TipsPhD32 WORKING WITH YOUR SUPERVISOR (4) Some supervisors like face-to-face meetings Some prefer exchanges You 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)

33 D.Gile TipsPhD33 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


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