Digging Deeper, Zooming Out: Reimagining Legacies in Medical Education
Bibliographic record
Abstract
Although the wide-scale disruption precipitated by the COVID-19 pandemic has somewhat subsided, there are many questions about the implications of such disruptions for the road ahead. This year's Research in Medical Education (RIME) supplement may provide a window of insight. Now, more than ever, researchers are poised to question long-held assumptions while reimagining long-established legacies. Themes regarding the boundaries of professional identity, approaches to difficult conversations, challenges of power and hierarchy, intricacies of selection processes, and complexities of learning climates appear to be the most salient and critical to understand. In this commentary, the authors use the relationship between legacies and assumptions as a framework to gain a deeper understanding about the past, present, and future of RIME.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.023 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 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".