“Challenging” Research Practices: Turning a Critical Lens on the Work of Transcription
Bibliographic record
Abstract
This article interrogates transcription work in the context of qualitative research. Although it is common practice in academe for someone other than the researcher to transcribe tapes recorded for purposes of data collection, the author argues the importance of researchers taking seriously the ways in which the person transcribing tapes influences research data. She suggests that the transcriber's interpretive/analytical/theoretical lens shapes the final texts constructed and as a result has the potential to influence the researcher's analysis of data. Specifically, the article explores the experiences of Ken, a person hired to transcribe audiotapes of focus group interviews conducted for a larger research study. The numerous challenges Ken faced during the work are addressed. His use of voice recognition software to simplify the task is discussed as well as the educational potential transcription work holds for graduate students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.262 | 0.309 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.032 | 0.159 |
| Scholarly communication | 0.042 | 0.046 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".