Improving Accuracy of Transcripts in Qualitative Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Everyone who has worked with qualitative interview data has run into problems with transcription error, even if they do the transcribing themselves. A thoughtful, accurate, reliable, multilingual transcriptionist with a quick turnaround time is worth her or his weight in gold. In this article, the authors examine some transcription circumstances that seem to bring about their own consistent set of problems. Based on their experiences, the authors examine the following issues: use of voice recognition systems; notation choices; processing and active listening versus touch typing; transcriptionist effect; emotionally loaded audiotaped material; class and/or cultural differences among interviewee, interviewer, and transcriptionist; and some errors that arise when working in a second language. The authors offer suggestions for working with transcriptionists as part of the qualitative research team.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.456 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it