Bibliographic record
Abstract
Almost two decades ago, Janice Morse launched this conference, and QHR as a venue for qualitative health research scholarship. The health research climate was then resistant to qualitative research methods. Qualitative scholars portrayed the rigor of their work in traditional scientific language and justified qualitative approaches in opposition to quantitative approaches. Emphasis on difference expanded as we embraced paradigm debates and argued about location of particular research approaches. Increasingly, I have been challenged to reconsider the boundaries between and among research methods and paradigms as I struggled to answer my most pressing emerging questions. The focus of this presentation is consideration of how the position of qualitative research has shifted over the past 20 years. Based on my program of women's health research, I will discuss the appeal and pitfalls of concrete distinctions, the challenges of flexibility and convergence, and the necessity of moving beyond difference toward a complete research toolbox that is useful for improving health.
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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 | 0.544 | 0.433 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.028 | 0.146 |
| Scholarly communication | 0.048 | 0.063 |
| Open science | 0.012 | 0.032 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".