Expert qualitative researchers and the use of audit trails
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
BACKGROUND: Determining the credibility of qualitative research findings remains a contested area and leaves the way open for additional theoretical and methodological discussion. AIMS: In this paper we focus on audit trails and confirmability, within the context of 'expert' qualitative researchers. Having outlined the audit trail process, we develop existing arguments about the 'expert' qualitative researcher. We then juxtapose the two, highlighting a number of issues in an attempt to advance the debate. DISCUSSION: These issues discussed are: (1) The shifting sands of methodological orthodoxy - the historical context in which audit trails emerged. (2) The individual construction of logic. (3) 'Grounded in the data' or 'going beyond the words'- the key differences between descriptive and interpretive findings. (4) The singular relationship between qualitative researcher and their data. (5) The growing acknowledgement that method alone is insufficient. (6) The challenging example of visionaries. CONCLUSION: We argue that using audit trails as a means to achieve confirmability of qualitative research findings is an exaggeration of the case for method, and may do little to establish the credibility of the findings. We also introduce a preliminary case for testing the credibility of theory induced by expert qualitative researchers, in part by means of its usefulness; its 'fit and grab', rather than by the researcher's adherence to contemporary methodological orthodoxy. In other words, the absence of audit trails does not necessarily challenge the credibility of qualitative findings, particularly if an expert qualitative researcher produced the findings.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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