Selecting a
topic for PhD research in interpreting
Daniel Gile
Université Lumière Lyon 2
Published in
Gile, Daniel, Helle Dam, Bodil
Martinsen and Anne Schjoldager
(eds). 2001. Getting Started in Interpreting Research.
Amsterdam/Philadelphia: John Benjamins. 1-22.
1. Introduction
Most scientific disciplines are essentially defined on
the basis of their research objects (physics studies the physical aspects of
reality, zoology studies animals, astronomy studies celestial bodies,
linguistics studies language), and at discipline level, find themselves
tackling specific research questions and topics as a matter of course. At the
level of institutional bodies - such as research laboratories or academic
departments - many research questions and objectives are determined by external
actors such as government agencies, industrial sponsors and other funding
organisations, and by circumstances (obvious examples can be found in medical
research). At individual level, researchers working within these bodies are
often assigned to research topics on the basis of their institution's needs. In
other cases, experienced individual researchers take up research topics and
objectives in some sort of natural sequence, based on their previous research.
Selecting
a topic for research is therefore not necessarily problematic. However, when
beginning researchers, and more specifically (prospective) PhD students, have
to select their own topic (sometimes they are given a list of potential
research topics by their supervisor - Cenková's paper
in this volume refers to a similar system for MA theses), it turns out that it
is a very difficult task for them, especially in the field of interpreting. In
fact, a sizeable proportion of PhD students never manage to take off, precisely
because they fail to overcome this problem, and in spite
of the absence of precise statistics, it is probably fair to say that this
accounts for a large proportion of aborted dissertations. This paper sets out
to provide some general guidance to prospective PhD candidates in the selection
of a topic for research, with some emphasis on and illustrations from the field
of interpreting.
2. Expectations from a research project
Research is basically a target-oriented activity, and
it is important for beginning researchers to know what the expectations are in
order to choose an appropriate target for their endeavour.
Essentially,
science is a systematic, rule-bound, collective exploration of reality. A
prevailing norm in the scientific community suggests that individual research
efforts should contribute to such exploration by adding and/or correcting
and/or consolidating either knowledge or a potentially useful tool for
knowledge acquisition.
In
concrete terms, such contributions can take three forms:
2.1
Empirical contributions
These can be the discovery of unknown physical, social
and behavioural entities and/or the unveiling of facts describing their nature
and behaviour. In the field of interpreting, to take just a few examples of
what could be a very long list, such empirical contributions can take the form
of new information about:
·
language-specific
interpreting strategies (Dawrant 1996),
·
the personal
history and linguistic behaviour of "truly bilingual" conference
interpreters (Thiéry 1975),
·
progress in the
interpreting students' ability to listen and speak at the same time (Pinter
1969),
·
inter-subject
variability in the vocabulary used to interpret a source speech (Lamberger-Felber 1998),
·
the
interpreter's working memory span (Padilla 1995),
Note
that generally in the scientific establishment as a social institution, while new empirical findings are given the
highest priority, corrections of
previous findings are also considered very important, and confirmation of previous findings by replication is indispensable.
In routine scientific activity, uncovering new information is therefore not a
standard requirement. At PhD level, some
degree of innovation is generally required, but this need not be a new theory,
a new model, or the discovery of new laws. Prospective PhD students should keep
this in mind in order to avoid setting their sights too high unnecessarily.
2.2 Conceptual ('theoretical') contributions
Another acceptable type of contribution is conceptual, in the
form of new theories, new questions, new hypotheses to test, new analyses of
known facts or findings: why do so many errors occur in source-speech segments
which do not present any measurable objective difficulty (Gile 1989)? Why is it
more difficult to interpret read speeches than impromptu speeches (Déjean Le Féal 1978)? Why is
quality perception by delegates highly variable (Gile 1999)?
Note
that generally, conceptual contributions generate empirical research, which
leads to empirical findings, whereas the reverse is not true.
It may
be worthwhile pointing out that in early interpreting research, most of the
conceptual contributions were introspective, and published in the form of
essays, with few references to the literature from the relevant cognate
disciplines, and few attempts to actually test ideas. Over the past ten to
fifteen years, the paradigm has shifted towards a more sophisticated form of
research. Introspection-based speculation has been replaced to a large extent
by conceptual research more in line with the behavioural sciences, referring
extensively to existing theories as reflected in the literature, with a strong
interdisciplinary component involving mostly linguistics and the cognitive
sciences (see for example Dillinger 1989, Setton 1997). Prospective PhD students should be aware that
a theory-oriented project now involves much reading of theory in cognate
disciplines, and assess the feasibility of their project accordingly (see
Section 3).
2.3 Methodological contributions
Exploration of reality in all its complexity is a
difficult endeavour, and one important component of scientific work is to
develop tools that help explore reality in a more sensitive, accurate and
reliable way. Therefore, the value of research projects can be assessed not
only on the basis of new information they collect and new
conceptual/theoretical analyses and entities they introduce, but also on the
basis of new exploratory tools they develop. Such contributions can materialise
as new experimental designs, statistical procedures, the development of
metrics, of measurement tools, of analytical procedures, of classification and
categorisation tools. For example, Pöchhacker (1994)
designed a tool to measure parameters of source speeches relevant to
interpreting difficulty, and Tommola and Hyöna (1990) looked at the feasibility of
the use of pupil dilation measurements as an indicator of mental load during
interpreting.
As is
the case of conceptual/theoretical contributions, methodological contributions
are generally associated with empirical findings which support them. Without
them, it is difficult to assess the value of a tool.
2.4 What is the minimum required contribution
for a research study?
One frequent misunderstanding among beginners is the
idea that a research project has to "find an answer" or "solve a
problem". Associated with this misunderstanding is their somewhat
unrealistic ambition, often expressed in the introduction of dissertations, to
"bridge the gap between theory and practice", to "determine/develop
the best training methods", to "show the mental operation of
interpreting", to "see how the interpreter's personality is expressed
in his/her interpretation style", to "show that performance when
working into a B language is better/worse than working into an A
language", to "show that a particular way of using a computer in the
booth is most efficient", etc. Reality is generally far too complex to
allow a single study to find complete and final answers to such questions. Most
studies 'only' contribute one or a few bricks to the building which is being
erected by many other members of the community, and more comprehensive (albeit
generally provisional) answers generally result from the efforts of dozens, if
not hundreds of researchers over long periods. This is often a disappointment
to beginners, who may be surprised when their supervisor tells them after they
have worked on their thesis or dissertation for some time that they can stop,
because they have "enough" for the degree.
This
disappointment is somewhat puzzling at first sight: while reviewing existing
literature in the course of their preparatory work, budding scholars have ample
opportunity to realise that previous theses and dissertations do not provide
comprehensive answers either. Expectation bias introduced by the strength of
their own motivation in starting a doctoral project may be the reason for their
reaction. Be it as it may, calling the attention of beginners to the fact that
individual research projects generally make small
contributions to the community's collective exploratory endeavour may help them
accept more modest objectives than those they would have contemplated
otherwise.
3. Topic selection and feasibility
One further point to remember when selecting a topic
for research is that science is 'opportunistic', in the sense that its
objectives and methods adapt to what is feasible, gradually extending the
territory and pushing the limits further as more tools, concepts, financial and
human resources become available. Generally, doctoral students who do not take
on board existing constraints when planning their study either do not manage to complete their dissertation, or find
themselves with a flawed end-product. Frequent feasibility problems include the
following:
3.1
Lack of time
MA and PhD regulations often impose limitations on
time-to-completion. Besides these formal constraints, each researcher also
takes part in life outside the academic microcosm, and experiences other
motivations and constraints which may compete with the demands of doctoral
work. Some PhD candidates need the degree for their career, in which case this
requirement sustains their motivation, but imposes time constraints of its own
(for instance if their scholarship or grant has a set duration, or if they need
to complete the degree within a certain period in order to have an employment
contract offered or renewed). Many PhD candidates also have an important
professional or personal activity outside university. In the latter case,
especially in the field of interpreting, where successful professionals may be
very busy and earn their living handsomely, once the coursework is over, it may
be difficult for PhD candidates to sustain their motivation, as other
activities become increasingly important. As time goes by, more interpreting
work, and possibly an enlarged family and more active social life make the
benefit associated with completing an advanced degree less attractive at the
required cost in time and efforts, and many PhD candidates drop out.
Two
distinct but interdependent time constraints therefore need to be addressed
when selecting a topic (actually, when considering the feasibility of doctoral
studies for a given prospective doctoral student): total duration to
completion, and total availability of time on a regular basis (daily, weekly or
monthly). A project involving much field work and/or laboratory work is not
feasible if the candidate is too busy to spare the necessary time slots. And
yet, this consideration is often neglected. Similarly, a project designed around
events lasting several months or longer (for instance, when studying progress
made by interpreting students over two years of studies) or occurring
periodically with long intervals in-between (such as graduation examinations)
may require several years for data collection even if it is well planned and
implemented, let alone if there are problems with the data collected the first
time.
Beginners
also tend to underestimate the time required to draft the dissertation. Many
believe that once the data have been analysed and results are available,
'writing it up' is fast and effortless. And yet, writing a document of up to
several hundred pages presenting a large volume of information and thoughts in
a clear, precise style and along a logical and convincing path which takes the
reader from a general introduction to the conclusion is a lengthy process,
which may take several months to the better part of a year even if the
student's effort is sustained. Actually, many a PhD student who has completed
his/her design, data collection and data analysis has given up when the 'only'
remaining task was to "write up" the dissertation.
3.2 Lack of baseline expertise
Another issue which arises in any study which involves
transdisciplinary components is baseline expertise,
both declarative ('knowing about things', in particular knowing about relevant
existing theories and facts) and procedural ('know-how', for instance as
regards experimental design or data processing), insofar as the project may
require expertise which the researcher does not yet have. Lack of awareness of
the gap may lead to aborted projects (generally when the problem arises in the
early stages of actual work on the concept, and the researcher finds
him/herself unable to overcome it), or to seriously flawed studies (generally,
when awareness of the problem arises at a later stage, and the researcher
decides to go ahead with the study and complete it nevertheless). In the field
of interpreting, gaps in declarative expertise are often found in psychological
and linguistic concepts, theories and tools, and there are frequent weaknesses
in procedural expertise as regards inferencing (see Gile's second paper in this volume), let alone experimental
design, statistical data processing, linguistic analysis of the corpus, etc.
Note
that such lack of expertise may also be found in the supervisor in charge,
whose own background may be of little relevance to the dissertation's topic and
paradigm, and who was assigned or chosen as a supervisor because no one else was
available locally (also see Section 4.2.3).
3.3 Lack of access to the literature
A pandemic problem in interpreting research is
difficult access to the relevant literature. Few university libraries, even in
well-known translation and interpretation schools in West-European countries,
let alone academic departments not specialised in translation and
interpretation, offer adequate coverage of interpretation literature.
Ironically but understandably, it is much easier for interpretation students to
find specialised texts in cognitive psychology and linguistics. Many research
projects by serious scholars from the adjacent disciplines have gone astray
because of a lack of insight into the mechanisms of interpreting, whereas more
available literature might have prevented this from occurring. The lack of
availability of interpretation literature has also generated much repetition
and loss of opportunities within the field.
3.4 Lack of access to subjects and/or
materials
Difficult access to subjects is a well-known problem
to all researchers in the field and is mentioned time and again in the
literature (though personal accounts in this volume do not confirm this - see
the conclusion at the end of this book). The difficulty arises not only because
of the small
size of the total population of conference interpreters (court interpreters and
community interpreters are much more numerous in many countries) and because it
is spread out geographically, but also due to the interpreters' reluctance to
serve as subjects for research. Authentic source speeches can be found, but
those having precise pre-determined linguistic, informational and other
characteristics are sometimes hard to come by, and so are recordings of their
authentic target-speech renderings, with the exception of those that have been
broadcast on radio, TV or the Internet.
Some supervisors can help students plan their work
adequately (see in particular Cenková's paper in this
volume on the guidance provided institutionally at MA level at the Institute of
Translation Studies at Charles University, Prague). Others consider that it is
up to the students to take responsibility for their work. Still others are too
overloaded to be able to devote to each of their students as much time as would
be required to check feasibility thoroughly. Finally, as mentioned above,
supervisors may find themselves in charge of a student working on a topic with
which they are not familiar, and do not have the necessary knowledge to guide
him/her efficiently through feasibility analysis. In all these cases, students
have to take their fate in their own hands.
4. Dealing with feasibility issues: project
planning
The best way to avoid the trap of feasibility problems
arising unexpectedly after the project has started and jeopardising its success
is to select one's topic only after careful consideration of the requirements
and potential problems versus available resources.
4.1 Areas of interest, topics and objectives
The first step would be to identify one or several
areas of interest, such as interpretation quality, interpretation training, the
interpreter's personality, the interpreter's status in society, etc. In each
such area of interest, one or several possible topics can be considered. For
instance, in the area of interpretation quality assessment, Collados
Aís (1996) chose to focus on the effect of monotonous
intonation on quality perception, which is just one out of the many dimensions
of quality. In the area of cognitive abilities of interpreters, Padilla (1995)
checked their memory span for one type of data, again a very specific location
in a wide space of possibilities.
Within
each topic, specific objectives
should be considered. These can be exploratory, as was the case Thiéry's doctoral dissertation (1975) devoted to an initial
description of the personal history and behaviour of 'true bilingual'
interpreters. They can also be focused around specific questions, as is the
case of Collados Aís's
dissertation mentioned above. They can also consist of tests of a hypothesis, a
series of hypotheses or a theory.
4.2 Method and resources
No final decision about a research objective should be
taken before a method is selected and its associated requirements are assessed
against available resources. Such an assessment involves concrete planning, far
beyond the choice of a general approach, so that precise needs in terms of
resources are known: prospective PhD candidates should, inter alia, decide what background literature to explore, see
where it can be found, what subjects and/or materials are relevant and where to
find them, what precise method(s) is/are going to be used, calculate the
approximate time required to go through the various steps of the study, and see
whether all other required resources are available. In particular, the
following important areas deserve to be highlighted:
4.2.1 Baseline expertise
If the method contemplated is theoretical discussion
or development of a particular theoretical entity, researchers should make sure
they have access to the relevant background texts, understand the languages in
which these are published, and have enough expertise in the relevant field. For
instance, the theoretical model of simultaneous interpreting adapted by Moser
in her 1976 dissertation from another model by Massaro
required theoretical knowledge in cognitive psychology. It is important to
realise that minimum baseline expertise may call for much preparatory learning,
up to several years of coursework in linguistics, cognitive psychology,
neurophysiology, etc. Browsing through a few textbooks or papers is not enough
to turn a novice into a genuine linguist, cognitive psychologist or
neurophysiologist. Guidance through discussions and comments with and by
experts is generally necessary to acquire a coherent, well balanced and correct
perception of the theories, thinking modes and research methods that make up
the relevant fields. The same applies to experimental design. Reading
descriptions of various classical designs in textbooks is not enough, no matter
how intelligent the students are. Implementing the reasoning process while
avoiding the numerous traps requires hands-on guidance and correction until the
skills are truly mastered.
If
prospective PhD candidates do not have the required baseline expertise, they have
three options: acquire it, enlist the help of an expert, or choose another
topic. If their supervisor happens to be an expert in the field, s/he may help
decide whether acquiring a minimum level of expertise is feasible. Without such
help, the decision to go ahead with the study is risky.
4.2.2 Sample representativeness
In any research endeavour which scrutinises a selected
part of the entity to be explored, that is a sample of the population
(the name traditionally given to the target entity), it is essential to know
whether there is good reason to believe it is representative of the whole
population, in which case it said to be a "representative sample", or
whether it represents a sub-population that may differ in some relevant
characteristic from the overall population. It is important to understand that representativeness is only loosely dependent upon sample
size. For instance, women interpreters probably make up well over 50% of the
population of interpreters, but may differ from the rest of the population
(male interpreters) as regards social behaviour, self-image, personal
ambitions, etc. In a study focusing on such parameters, a sample composed of
female interpreters, be it 50% of the total population of interpreters, could
only be taken as representative of the female sub-population of
interpreters, and any inference on the whole population would be justifiably
criticised. On the other hand, a well-chosen sample of a few thousand people
yields fairly accurate estimates of behaviour in a population of millions of
people.
In
interpreting research, the lack of access to subjects and the limited volume of
empirical research conducted so far make this issue particularly sensitive,
because there is little data to help determine the limits of generalisability. To what extent and in what areas is it
legitimate to extrapolate from one type of speech to another, from one language
to another, from students to professionals, from a particular working
environment to another?
Two
fundamental rules can be recommended in this respect:
Firstly, use all available information to decide whether a sample of
subjects to whom you have access can be representative of your population in
respect of the phenomenon you are studying; if not, consider changing the focus
of your study. For instance, suppose you are interested in the study of
automated cognitive processes of a complex nature in interpreters, and it turns
out that you only have access to student interpreters. Reading the literature
and consulting with experienced researchers may make you decide that students
cannot be taken as representative of the population of interpreters with
respect to the focus of your study. In such a case, it does not make sense to
go ahead with the project. A somewhat different situation occurs when you want
to analyse the personality of interpreters, and find out you only have access
to staff interpreters in an international organisation. An analysis and some
reading of the relevant literature in sociology and psychology may make you
decide that the personality of staff interpreters may differ from that of
free-lancers. In such a situation, you can go ahead with your project, but with
a narrowed-down objective, namely to study the personality of staff
interpreters only.
Secondly,
be cautious when generalising from your results. Clearly, in the project about
the personality of staff interpreters, you can only generalise to staff
interpreters, and possibly only to staff interpreters in certain types of
organisations. The issue of generalisability is
tricky: after all, when implementing a policy of scientific scepticism and
caution in strict terms, one could argue that unless a strict random sampling
procedure is used, something which is not feasible in the interpreting research
environment (and in most behavioural disciplines), there is always the
possibility that some hidden sampling bias exists, and that only claims about
individuals in the actual sample studied are legitimate. The second best (and
the only feasible) strategy in most cases is to consider all available evidence
and reflections by fellow researchers, to decide how far one wishes to
generalise, and to explain in one's text whether, why and with what
reservations one could consider that the findings also apply beyond the
'generalisation perimeter'.
At
this point, and as long as no comparative data is available to help determine
the limits of generalisability, such decisions will
be largely subjective. Making them carefully on the basis of all available data
and making the limitations clear to the reader (see Schjoldager
in this volume) are the acceptable way to proceed.
4.2.3 Sample size
One other matter which deserves particular attention
in interpreting research is sample size. In an empirical study, the number of
subjects that can be observed and/or submitted to experimental procedures is an
essential parameter. In a hypothesis-testing endeavour, random variation makes
it difficult to contemplate any generalisation from very small samples, unless
intra-sample variability is small. This is a matter of common sense, and
mathematics only add precision to the principle: generalising on the basis of a
4-unit sample with measured values 2, 3, 3 and 2 is more reasonable than
generalising on the basis of a 4-unit sample with measured values of 2, 3, 75
and 60. This is particularly critical if the idea is to compare two samples and
decide whether they come from the same population, which is the basic idea in
most experimental procedures (for instance, when testing student performance
against professional performance, or performance when interpreting from an
A-language into a B-language vs. work in the opposite direction).
On
the other hand, if the question is whether some mean value (such as a metric
for quality, a physiological value, a proportion of a population displaying a
particular feature, etc.) is higher or lower than some threshold, or even
whether some mean value is 'rather high' or 'rather low', some generalisation
is possible even in a highly variable sample. For instance, suppose the
research question is whether names are truly problematic in interpreting, as is
often reported in the literature, and correct name-rendition rates are measured
in a random sample of subjects performing an interpretation task. If the
measured values are 30%, 85%, 50%, 60%, 25%, in spite of the small size of the
sample and its variability, it is clear that all subjects experience
difficulties with names, and performing statistical tests will not contribute
anything towards finding the answer to the precise question as defined above.
The
mathematics of probability theory and inferential statistics can give more generalisation power to
findings if more is known about variable distributions, especially if these are
close to the Gaussian curve (also called "bell-shaped curve" or
"normal curve"). For further technical details and precise
suggestions on sample size, read one of the many available books on practical
statistics for the behavioural sciences, and/or consult statisticians.
However,
as long as no unreasonable generalisations are made, there is nothing wrong
with studies on very small samples. Even case studies with single subjects are
legitimate scientific endeavours, especially when they serve as exploratory
studies. Many important scientific discoveries originated in case studies. In
fact, Bromley (1986, quoted in Coolican 1999:126),
has called case studies "the bedrock of scientific investigation",
while Robson (1993) argues that experiments should also be considered case
studies, which makes sense insofar as each is based on a single sample or set
of samples.
4.2.4 The availability of a competent supervisor
For historical, sociological and institutional reasons
(see Gile 1995), there are still few supervisors for research into interpreting
who have both research expertise, especially as regards empirical research, and
knowledge of the world of interpreting, and such supervisors with sufficient
competence for the particular topic the candidate has chosen may not be available
locally (the subject is discussed further in the context of interdisciplinarity
in Section 6.1 and illustrated in several contributions in this volume). In
such a case, candidates may decide to choose a topic for which there is a local
supervisor, or to enlist the help of experts from other institutions as
co-supervisors. Thanks to modern technology, it is even possible to work with
people from afar (see Mead, in this volume). However, there may be
institutional and personal obstacles: some universities may not allow
supervision by scholars from other institutions, or the main supervisor may be
reluctant to accept the very principle of co-supervision. It is also possible
to enlist informally the help of
experts from other institutions, but this is also associated with some risk if
the official supervisor's views are at odds with the informal supervisor's.
5. Strategies
Somewhat ironically, while doctoral training programs
are said to teach graduate students what research is all about and how to go
about it, in many cases, the PhD project, which is the culmination of the
learning process, may differ significantly from research projects as they are
conducted in the field. The essential reason for the difference is that while
in research as practised by qualified scholars, projects generally aim at
advancing towards solutions to research problems, PhD projects aim at
fulfilling academic requirements, and are driven by them and largely
constrained by the local academic environment and its norms. The two are not
always compatible, and a choice may have to be made. Doctoral projects are a
necessary gateway in academia and do not restrict their authors' freedom to
engage in any type of research they choose after
obtaining their degree. It may therefore be best, when students have long-term
academic objectives, to give priority for a time to the specific requirements
of the doctoral project rather than to norms of research as it is practised
more generally in the field. This implies in particular specific strategies
relevant to the selection of topics:
5.1 Feasibility as a priority
While research proper in a PhD project must be in line
with the prevailing standards of research, as mentioned above, the fundamental
aim underlying a PhD project is the acquisition of the degree. Consequently, the
candidate's priorities are not quite the same as those of non-degree research.
More specifically, adapting to environmental resources and constraints may take
precedence over the quest for a solution to a specific research problem, and
candidates would be well-advised to consider the possibility of choosing a topic most likely
to facilitate their doctoral work rather than a topic closer to their research
interests but more difficult to tackle at the PhD stage in their local
environment. In particular, as already mentioned, choosing a topic in line with
the expertise and interests of a locally available supervisor may be a wise
strategy, although before doing so, candidates should attempt to predict how
they will react to several years of work on a subject which may not be their
favourite.
5.2 Alternative routes
Planning alternative routes is another strategy
specific to research-for-a-degree as opposed to ordinary research. Although
some research projects are straightforward, when innovative or original
elements come in, problems and outcomes are somewhat unpredictable in spite of
careful planning. This is why it is wise to plan not only the implementation of
the initial action plan, but also alternative routes, which become increasingly
important as the project advances. If, during the early stages of a study, PhD
candidates discover that access to background literature is more difficult than
anticipated, or that their project requires baseline expertise that they do not
have and do not have the time to acquire, they can still choose a different
direction for their study without incurring dramatic loss of time and other
resources. If, however, they find after much investment that their material is
not suitable for their project, or that there is too much variability in their
data to find the answer that they had sought, a radical change may be much more
painful and/or costly.
Alternative
routes should be planned rather than improvised, the idea being to use to the
largest possible extent the work already done at the time the change in
direction is made. This means in particular thinking of several scenarios that
will be using to a large extent the same background literature, the same
material, and the same sample of subjects. Without such planning, there is a
risk of having to discard much of what was achieved before the insurmountable
obstacle was found.
5.3 Determining the scale of the study
The issue of alternative routes is linked to another
question, namely how and when to determine the scale of the study.
Theoretically, careful planning at all levels is highly recommended, and in the
traditional experimental paradigm, the scope of the study, including the number
of experimental groups and their respective sizes, as well as the corpus to be
used (most often source speeches), is supposed to be determined in advance.
However, in the field of interpreting studies, especially in the case of
time-constrained doctoral dissertations, this is not always possible, or indeed
wise, simply because of the difficulties involved in finding authentic speeches
and enlisting the co-operation of professional interpreters for research
purposes. Determining in advance in a non-modifiable way the scope of the study
in such terms may entail the risk of failure, if the sights are set too high,
or of missing opportunities for collecting valuable information, if they are
set too low. An open-ended design with minimum sample sizes and materials which
can also accommodate further materials and/or information may lead to better
results.
One
example is a study by Gile (1999) - not a doctoral project - on variability in
the perception of fidelity, which involved several steps and an incrementally
growing number of participants. The idea was to check variability in the
perception of interpreting quality by various groups, including interpreters
and non-interpreters, and depending on the mode of presentation of the target
speech (audio recording or transcript). Data on reactions in the written mode
(the transcript) was obtained incrementally by questionnaire, as interpreters
and other subjects became available. Data on reactions in the oral mode (the
audio tape) had to be collected on a large enough sample using a method and
environment ensuring identical conditions for all subjects. The opportunity
presented itself when I was invited to give a one day workshop on research into
conference interpreting at an interpreting school. I thus had enough time for
the experiment and a large sample of participants - it goes without saying that
deciding to wait for such opportunities is only an appropriate strategy if the
researcher knows that such opportunities will present themselves within an
acceptable timeframe. One reviewer of the paper reporting the study, who had a
traditional experimental background, commented that the study seemed to be
improvised, rather than planned. In fact, precise planning and the preparation
of materials was necessary in order to seize the
opportunity when it arose. Admittedly, there was some uncertainty as to when
exactly it would arise and what the precise size and composition of the sample
of subjects would be, but a close-ended study could probably have been
conducted only at a much smaller scale.
Sharing
corpora and using the same speech and some findings from completed research for
further studies is one way of increasing the amount of available, comparable
data. It is also, in a rather loose sense, a way of increasing 'sample size'
(but not in the strict sense, since the data may be available from a set of
distinct samples, with differences in the task and/or environmental conditions
under which it was obtained). The open-ended design is legitimate and
efficient, though one must be careful to identify the differences between
conditions in the various incremental stages so as to avoid making unjustified
inferences.
5.4 Canonical and non-canonical steps in
the research process
In general accounts of science, the point is made that
research is based on a theoretical foundation, insofar as one or several
theoretical account(s) of a phenomenon or set of phenomena are studied and then
either developed (in theoretical research) or tested (in empirical research).
In actual life, research often focuses on a pragmatic problem, without an
extensive theoretical basis: Is there a difference between the expectations of
various user groups as to interpreting quality? Do interpreters work better
from an A-language into a B-language or vice-versa? How useful are specific
conference preparation methods for interpreters?
While
some form of "theory" in a very general sense of the word is always
present behind the very questions, in empirical studies, it need not be as
comprehensive, cohesive and well-articulated as a theory presented as such in a
scientific text. In the humanities, and in particular in a humanities-oriented
view of Translation Studies (as opposed to a social-sciences-oriented view of
Translation Studies), the prevailing norms seem to require from doctoral
dissertations a substantial introduction, at least one chapter long, simulating
the researcher's route from theoretical considerations to the precise research
question(s). While it is necessary for PhD candidates to explore the
theoretical background to a research topic, as well as previous empirical
studies and their findings, making a final selection of the topic on this sole
basis may not be the most efficient strategy, because feasibility issues may
then force the student to backtrack. It is often better to start with one or
several tentative research questions, do some planning and check feasibility and previous work on the subject, and
defer the drafting of the detailed literature-based introduction to a later
stage, so that it can introduce the
reader to the actual research question tackled. In other words, while the
way the dissertation is structured seems to suggest that its author started out
with theoretical exploration of the issue, developed a set of hypotheses and
went on to test them, the actual order in which the research
project was carried out may be quite different.
6. Further issues
6.1 Interdisciplinary work
The idea of interdisciplinary work (meaning, in this
context, work on interpreting involving a substantial input from an adjacent
discipline such as psychology, linguistics, sociology, etc.) has become very
popular in the last decade or so, and many prospective PhD candidates are
attracted to the idea of working on cognitive or neurophysiological
topics. While a number of literature reviews and simple empirical studies at MA
level have been completed successfully, at PhD level, feasibility issues are
more problematic and should not be underestimated. In particular, in the
cognitive paradigm, experimental design at PhD level needs to be very strict
(see for example the sophisticated design of Dillinger
1989 or Liu 2001), and it takes specific training or much experience to acquire
the required expertise.
In this
connection, the following points deserve special attention:
- In
principle, doing interdisciplinary research at PhD level requires the input of
supervisors or co-supervisors from the relevant adjacent discipline. Such
supervisors may assume that the students have much better knowledge of that
discipline than is actually the case, be reluctant to coach PhD candidates into
the acquisition of basic knowledge or skills which they should normally have at
their fingertips at that stage, and/or require them to attend many courses to
bring them up-to-date; some of these courses may seem irrelevant or too remote
from interpreting-related matters. Candidates for interdisciplinary projects
should be aware of these possibilities and decide whether such requirements are
acceptable to them.
- In some
cases, especially when students are experienced interpreters themselves,
supervisors from adjacent disciplines may trust them to 'do the right thing'
without actually supervising them, and be too lenient when the completed work
is presented to the assessment committee, because of these students' special
status as 'outsiders' and as experienced interpreters. This may result in
weaknesses both as regards interpreting proper (due to the supervisor's
insufficient knowledge of the world of interpreting and of the associated
literature) and as regards the adjacent discipline.
- There may
also be differences between the student and the supervisor when choosing
metrics and when interpreting the data. For instance, in the interest of
rigorous measurement, non-interpreting researchers may insist on using formal
textual criteria such as propositional matching between source speech and
target speech to assess fidelity, without taking into account interpreting
strategies. In experiments having performance as the dependent variable, this
can be very problematic.
It is therefore particularly important, when
contemplating an interdisciplinary doctoral project, not only to make sure that
a supervisor from the relevant discipline is available locally, but also that
s/he is mentally available and
willing to help over the whole duration of the PhD work, and to ascertain what
his/her attitude and requirements are. When the dissertation is completely
within the paradigm of the adjacent discipline (as was the case in Padilla's
1995 dissertation on the interpreters' memory span), things are relatively
straightforward, but as soon as genuine interpreting work comes in, problems
arise easily. On the whole, interdisciplinarity seems
less problematic with linguistics and sociology, with their strong descriptive
and naturalistic component, than with cognitive psychology, which tends to
favour 'hard' experimental procedures (see the comments in Shlesinger
2000).
6.2 Scientific norms
Another issue which is far from trivial has to do with
the selection of scientific norms for interpreting research, since these are
diverse and evolve over time. In the mid-seventies to mid-eighties, the
prevailing interpreter-researchers' norms (see Gile 2000) were associated with
essay-type explorations of the applicability of the prevailing paradigm (in the
form of "why the théorie du sens also
applies to Korean" - see Choi 1986), as opposed to strictly experimental
studies or to theoretical development in the strict sense such as done in Moser
(1976), in Mizuno's work on a 'dynamic model of simultaneous interpretation'
(1993) and in Setton's pragmatics-based theory
(1997). Towards the end of the eighties, a shift occurred, with a strongly
interdisciplinary trend, with respect not only to the topics and methods used,
but also to scientific standards. As mentioned above, the now central position
of cognitive psychology among the proponents of interdisciplinary research is
associated with a strong position of the experimental paradigm in the strict
sense, and within the ranks of interpreter-researchers, there seems to be a
strong feeling that experimental research is the only one which truly meets the
requirements of Science.
The
experimental paradigm certainly provides a relatively powerful rationale to
deal with confounding variables (variables which exert influence over the
phenomena under investigation but which are not controlled by the
experimenter). However, it also entails a number of drawbacks other than the
well-known issue of ecological validity (to what extent do laboratory
conditions reflect the genuine phenomena they are supposed to measure?), and is
being challenged from within the ranks of cognitive psychology - for an
interesting discussion of the issue, and the presentation of an alternative
qualitative approach, see Coolican 1999.
Besides
this fundamental rationale, practical reasons such as the lack of access to a
sufficiently large number of subjects, the lack of knowledge about variable
distribution, the lack of knowledge about inter- and intra-subject variability,
the lack of validating evidence for performance metrics, etc., may erect severe
methodological and feasibility obstacles in the way of the conventional
hypothesis-testing type of experimental paradigm in the world of conference
interpreting, and reduce considerably its potency.
It
is important to remember that the experimental paradigm is by no means the only
legitimate one in science, and that naturalistic paradigms are standard in many
disciplines acknowledged as scientific (including astronomy, geology, botany, zoology,
epidemiology, etc.). As regards behavioural disciplines, and in particular
cognitive psychology, quoting from Coolican (1999)
again, "it is perfectly possible to test hypotheses without an experiment.
Much psychological testing is conducted by observing what children do, asking
what people think and so on." (p.12). He explains that "scientists
are pragmatic in their progress of knowledge, rather than following the rigid
code which is often presented as the ideal to work towards in psychology
methods courses" (p.197). Allport, a promoter of
psychology as a conventional science, explains that "We should adapt our
methods so far as we can to the object, and not define the object in terms of
our faulty methods" (quoted in Coolican
1999:198). Going further into the critique of the conventional experimental
paradigm is a feminist perspective which sees it as
characteristic
as a male approach to research…: preoccupation with quantifying variables; an
emphasis on control, mastery and manipulation; a tendency to remain distant
rather than be involved with the subjects of research; a preference for
gadget-oriented research over naturalistic enquiry; competition and ego
building. (Coolican 1999:207).
Without
necessarily going as far in the critique of the experimental paradigm, PhD candidates
should realise that some margin of freedom in the choice of their scientific
norms is legitimate. Unless forced to do otherwise for practical reasons, for
instance when enrolled in doctoral programs which impose the conventional
hypothesis-testing experimental approach, PhD candidates should ideally be free
to choose one out of several scientific paradigms, to be determined according
to their own tastes and as a function of feasibility - with the proviso that
their underlying rationale is solid. In that respect, the paradigms of
sociology, with their emphasis on naturalistic studies, which are not less
rigorous than experimental studies by nature, may provide a good reference to
present to reluctant prospective supervisors.
7. Conclusion: preparing students for research topic selection
As can be seen, selecting a topic for a PhD project is
not to be seen as a short, straightforward preliminary to the study proper.
Rather, it is a planning process, sometimes complex, often lengthy, which may
last well into the actual literature review, data collection and even data
analysis stages (as illustrated by Dubslaff's and Schjoldager's accounts in this volume), sometimes forcing a
change in direction somewhere along the line (see Dubslaff,
in this volume). Optimising the process requires much reflection, as well as a
flexible and realistic attitude. In interpreting research, PhD students are
often experienced interpreters who do not need the doctoral degree for their
career and who undertake their PhD project with the purpose of solving an
interpreting problem or finding an answer to a question. This may make it more
difficult for them to come to terms with the reality of research, and with the
idea that their project will probably not bring forth the ultimate solution or
answer.
Is
the difficulty of the topic selection process universal? It is obviously a
critical process, and my own experience as a supervisor and the experience of
fellow researchers in several other disciplines, including translation studies,
literary studies and linguistics, with whom I have
discussed the issue, seem to suggest that it may well be difficult for many
students. In established empirical disciplines, the process should be easier.
While studying for a second degree, and even for an undergraduate degree,
students are introduced to research methods, and their study of the evolution
of concepts and theories in their discipline makes them analyse closely and
critically the designs and methods used throughout its history. Often, they
also have to design and conduct their own empirical projects in the course of
their studies. When their turn comes to select a topic for a PhD, they already
have a basis for their planning and decision process. Moreover, in such
disciplines, a number of research paths are well charted out and validated, and
there are many qualified supervisors to guide the students in the appropriate
direction.
In
interpreting research, where students often have to chart their own course and
do not have the benefit of a syllabus which prepares them naturally to
research, besides training in critical reading (see Gile's
other paper in this volume), specific training in topic selection/research
planning is probably a useful component of coursework for beginning
researchers. Students are presented with a research question or topic and asked
to plan a course of action for a PhD project that would seek to explore it and
find a specific answer or solution, listing all expected difficulties,
available resources, possible strategies, alternative routes, etc. Their
analysis and design are then discussed in the classroom, and compared to actual
research done on the same topic or question. In the absence of such courses,
students should be encouraged to seek as much help as possible from their
supervisor, as well as from other experienced researchers.
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