Best Practices for Speech Corpora in Linguistic Research Workshop ...

The ‘externality’ of DA arises from its willingness to
identify specific features of talk (classically, particular
speech acts) and apply these as what CA would see as a
priori categories. The advantage of such an approach is
that it allows the sort of coding that makes extensive data
sets accessible to the analyst; the disadvantage is that it
does so at the expense of failing to capture aspects of the
construction of the talk, with the result that it may all too
easily miss what is actually getting done through the talk.
It is this focus on action that characterises CA and explains
its insistence on the importance of the sequential unfolding
of interaction. Schegloff (1991, p. 46) captures this essential
relationship well:
. . . the target of its [CA’s] inquiries stands where
talk amounts to action, where action projects consequences
in a structure and texture of interaction
which the talk is itself progressively embodying
and realizing, and where the particulars of the
talk inform what actions are being done and what
sort of social scene in being constituted.
The advantages of this approach is that it enables the
analyst to understand what is being achieved through the
talk in a way that is not open to the discourse analyst
using coding to analyse large data sets; the disadvantage is
the demands it places on the analyst in terms of time and
resources. The transcription system itself demands close
attention to minutiae of delivery and the process of collecting
data is often a slow and painstaking process, which
might be described in terms of ‘tracking the biography of
the phenomenon’s emergence’ (Jefferson, 1983, p. 4) or
‘having accumulated a batch of fragments’ (ibid p. 16) .
Our basis for bringing these apparently incommensurate
approaches together lies in finding a way of applying CA
in order to identify an ‘action’, then using DA in the form
of a coding system based on pragmatic features to identify
patterns across stretches of talk that identify this action,
then applying CA to the instances thus identified in order to
check the accuracy of the specified pattern. While no CA
practitioner would accept this as a legitimate analysis in itself,
since it will always be possible that other things are
being accomplished through the talk, it does allow specific
actions to be identified and thereby makes it possible to develop
increasingly rich pictures of how particular actions
are distributed through the talk. What follows focuses on
the tools that can be used to maximise the benefits derivable
from an action -based analysis.
3. Data
The data used for this project are drawn from audio
recorded interdisciplinary scientific research project meetings
ranging from large collaborative funded projects with
at least 6 participants in each meeting to interdisciplinary
PhD supervision meetings consisting of two supervisors
and a student. The disciplines represented in these meetings
consist of mathematics, statistics, biology and bioinformatics.
The data have been collected since March 2011,
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producing about 120 hours of audio recordings to date as
part of a collection that will continue to grow as we follow
a number of research projects to completion. What we are
presenting here is based on only small part of the data that
have been transcribed (amounting to 20 hours to date).
It is also necessary to emphasise that this represents
an early stage in the project. Development is currently
focused on an action in which the speaker introduces a
question and then follows this with a series of turns leading
to a suggestion. What makes this a particularly attractive
starting point for our analysis is that the sequence is marked
by turns with so in the turn-initial position (‘so-clusters’),
which are easily identifiable in the data. Extract 1 provides
an example this action (all names are pseudonyms):
Extract 1
01 ALF was it a strict criterion for it
02 ROY or no not very strict
03 ALF so you don’t think it’s normal
04 it’s (xxx) fourteen that’s
05 really the
06 ROY I probably could find more yes
07 I mean I didn’t use any strict
08 criteria just I applied some
09 (xxx) two or three (xxx) also
10 my eye each if I believe.
11 ALF yeah so it should be about the
12 number.
13 GARY yeah
14 ROY depends you know if I make it
15 less strict (xxx) fifty (xxx).
16 ALF so if say eighty percent of them
17 are thought to be affected by
18 the wash if we did the whole
19 mock wash micro array data it
20 would allow us to identify
21 twenty genes that are affected
22 by pulse so we don’t know
23 whether that’s relevant it’s
23 worth its worth finding out (4.0)
In terms of interdisciplinary talk, once the analysis is
complete it will be interesting to see how this action
is distributed in the data. If, for example, quantitative
analysis reveals that such exchanges are inter-disciplinary
(as opposed to intra-disciplinary), this would provide prima
facie evidence of genuine interdisciplinary exchanges. It
would also enable us to collect examples of this across
different data sets in order to understand more about
how such sequences work towards the building of shared
understanding and action.
At this stage we are working with basic transcriptions
of the sort illustrated above and limiting more delicate
transcription to examples of the relevant action, though
the differences between these can be considerable, as a
comparison of Extract 2 with its ‘equivalent’ in lines 05
and 06 in Extract 1 demonstrates: