TIPS FOR PhD RESEARCH IN
TRANSLATION AND INTERPRETING
Daniel Gile
daniel.gile@yahoo.com
www.cirinandgile.com
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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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…)
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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?
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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
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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)
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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.
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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?
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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?
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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?
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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?
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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….?
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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
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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)
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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
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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
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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
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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
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WORKING WITH YOUR SUPERVISOR (4)
Some supervisors like face-to-face meetings
Some prefer email 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)
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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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