Expert qualitative researchers and the use of audit trails
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.
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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 | 0.634 | 0.793 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".