Bibliographic record
Abstract
While many reject the label of “soft science” that is often used derogatorily to characterize qualitative research in the health sciences, we propose we should embrace and redefine the label, asserting its methodological potency for understanding health and health care, and as an expansion of the scientific field. In this paper, we reflect on the strategies we have developed over the last 25 years, as we worked together as teachers of qualitative research at the graduate level in the health sciences. The main outcome of our collaboration was the establishment and development of the Centre for Critical Qualitative Health Research at the University of Toronto. As former directors, we reflect on how to practice and teach in a world of limited literacy in qualitative research; how to make an institutional place for critical qualitative research in the health sciences; how to understand critical qualitative research as a potent form of “soft science”; and how we positioned ourselves in a marginal scientific location, at the edges of the academic system, while celebrating our potent “soft” methodologies and methods.
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.
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 | 0.221 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.163 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".