Bibliographic record
Abstract
Reflexivity has gained paramount status in qualitative inquiry. It is central to debates on subjectivity, objectivity, and, ultimately, the scientific foundation of social science knowledge and research. Although much work on doing reflexivity by researchers and practitioners has been published, scholars have only recently begun to explore how one goes about teaching reflexivity in qualitative research. This paper contributes to the endeavour by first identifying challenges of teaching reflexivity. It then describes how I use an existing data set and hands-on learning as complementary strategies to teach reflexiv-ity in a course on qualitative interviewing. It concludes with a discussion of the implications of teaching reflexivity in sociology in general and in qualitative research methods in particular.
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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 | 0.466 | 0.532 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".