What can a journal editorial team do to strive for equity in health professions education publishing? Leading by example
Bibliographic record
Abstract
WHAT WAS THE EDUCATIONAL CHALLENGE?: Representation gaps in medical education publishing are widely recognized and may be attributed to epistemic injustice, defined as 'wrong done to someone in their capacity as a knower.' Although peer review is meant to ensure 'rigor,' some quality assurance practices can inadvertently silence entire populations and impede understanding of a field's foundational concepts. WHAT WAS THE PROPOSED SOLUTION?: reimagined rigor to include striving for a 'more equitable, diverse, and inclusive research system.' HOW WAS THE PROPOSED SOLUTION IMPLEMENTED?: framework for health equity, prioritizing change in our communication with contributors. WHAT LESSONS LEARNED ARE RELEVANT TO A WIDER AUDIENCE?: Since implementation, our journal has received feedback expressing appreciation for humanity and personal connection in our peer review, and we have observed increased publications from geographically marginalized authors. We believe our outcomes result from respecting marginalized authors' authority to pursue their own interests, concerns, and successes with respect to knowledge production. WHAT ARE THE NEXT STEPS?: We believe our approach can be adopted by other peer-reviewed journals. We invite application and critique of our framework to advance community development in creating relevant, accessible, and equitable knowledge production for all people.
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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 | 0.250 | 0.600 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.061 | 0.038 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".