Improving Accuracy of Transcripts in Qualitative Research
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.
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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 | 0.630 | 0.834 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".