HPE as a Field: Implications for the Production of Compelling Knowledge
Bibliographic record
Abstract
ISSUE: Research in education, including health professions education, has long struggled with the competing concerns of academic and practice-based stakeholders. Inspired partially by the work of Stokes and other theorists in science and technology studies, we propose that discussions about compelling research in health professions education might be usefully advanced by considering what it would mean if the community framed itself as a knowledge-producing field instead of aligning itself with either disciplinary or practical interests. EVIDENCE: Efforts to foreground disciplinary or practical interests in education research have been unproductive, leading to the privileging of one group's expertise at the expense of the other. Currently proposed principles and practices for responding to the divergence between these interests, such as knowledge translation or practitioner inquiry, have yielded comparatively little in the way of mutual satisfaction. IMPLICATIONS: As a field, health professions education research would not privilege either disciplinary or practical interests, nor would it attempt any sort of definitive blueprint for resolution to the tension. Rather it would regard these interests as inherently interconnected and, therefore, always in tension to varying degrees. The challenge for a field is not to resolve that tension but to harness it in productive ways through collaboration, negotiation, and compromise, through ever-shifting engagements that will not necessarily be comfortable but will nonetheless foster knowledge that resonates with all parts of the community.
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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 | 0.153 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.168 |
| Scholarly communication | 0.036 | 0.065 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".