Doing data: The status of transcripts in Conversation Analysis

Abstract

This article discusses the status of transcripts in Conversation Analysis. Repeatedly, the function and the epistemic state of transcripts have been the subject of discussions and reflections in Conversation Analysis. Drawing on a range of empirical examples taken from various authors, this article discusses the question of how present forms of visuality and multi-modality in the data material or the handling of artifacts can be captured in transcripts and how the problem of ‘representation’ of complex and interactive situations can be solved. It turns out that the evolution of recording media along with the medium shift in Conversation Analysis has altered the status of transcripts, effecting a twofold ‘in-between-ness’: transcripts inhabit the conceptual space between orality and literacy on the one hand and between artwork and figure on the other hand. This oscillating hybrid nature proves to be the central property of transcripts in present-day Conversation Analysis. Transcripts are described as scientific ‘objects of knowledge’ and discussed with respect to their methodological and epistemic status.

‘Um, er’: how meaning varies between speech and its typed transcript

“…Extract 1 Interviewee: I believe, and [anonymised name] believes, that these gravity wave events that we're seeing are, should be so, infrequent, Interviewer: mm Interviewee: that, err, we should not try to have lots and lots of coincidences and look for little changes in it we have to, and, we, Interviewer: mm mm [overlap] Interviewee: we have to just look for, err, do the standard bayesian statistics, mm, and, err, uhh, look in, in that way Interviewer: Right Interviewee: Uhm, and, err, I m-an-an-and that's never going to change 6 The development of transcription conventions in the social sciences has, in part, reflected the recognition that these choices should be of concern to social scientists. Gail Jefferson's conventions (Sacks et al, 1974;Jefferson, 2004), or variations thereof, are perhaps most commonly employed, to the point that they are regarded as the 'default transcription system' (Ayaß, 2015). Data transcribed in the strict Jeffersonian system is highly detailed, providing the reader with access to a wealth of information besides the actual words spoken by interlocutors, such as intonation, latching, breath sounds, timed pauses, etc.…”

GailBot: An automatic transcription system for Conversation Analysis

“…In short, using GailBot substantially reduces the time needed for CA transcription, but the exact amount of time reduction will depend on the particular conversation and transcriber. GailBot aims to produce human-readable transcripts of dialogue in a format designed by conversation analysts to refine and interpret qualitative data (Ayaß, 2015;Ochs, 1979). This differs from data annotation systems that aim to produce categorical annotations, usually for computational linguistics or machine learning applications (e.g., Apostolova et al 2010;Stenetorp et al 2012;Yimam et al 2013;Klie et al 2018;Tkachenko et al 2020Tkachenko et al -2021.…”

A Multi-Layered Framework for Analyzing Primary Students’ Multimodal Reasoning in Science

“…As Bezemer and Mavers [45] argued, even the process of transcription itself should be considered as a social meaning-making practice by which the empirical phenomena under investigation are reconstructed. Similarly, Ayaß [46] (p. 508) argued that transcription is 'a constitutive part of the empirical research process' whereby the visual and audio records of social events are often transformed into written text, largely guided by the transcriber's interpretive frames and intentions. In our case, we employed video analysis software (i.e., StudioCode) to support our analytical process by drawing our attention to particular groups of students and segments of interactions recorded on video.…”