Rethinking reflexivity, replicability and rigour in qualitative research
Bibliographic record
Abstract
This commentary re-examines recent proposals to define quality in qualitative research through a singular unifying framework, situating them alongside historical and ongoing debates in qualitative methodology. By juxtaposing different traditions, this piece highlights areas of tension between procedural notions of rigour and interpretive approaches that emphasise the co-constructed, context-bound nature of meaning. The discussion argues that quality in qualitative research cannot be captured by a single metric or universal rule. Reflexive approaches resist rigid frameworks, instead favouring a situational and evolving engagement with meaning. While efforts to promote transparency and accountability in qualitative research are valuable, researchers should adopt methodological criteria aligned with their epistemological commitments. We argue that qualitative research can be considered rigorous insofar as it is deeply reflective, explicitly contextualised and transparent about its interpretive manoeuvres.
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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 | 0.854 | 0.832 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.015 | 0.212 |
| Scholarly communication | 0.042 | 0.059 |
| Open science | 0.020 | 0.033 |
| Research integrity | 0.019 | 0.034 |
| 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".