The imperative for scientific societies to change the face of academia: Recommendations for immediate action
Bibliographic record
Abstract
As organizations that facilitate collaboration and communication, scientific societies have an opportunity, and a responsibility, to drive inclusion, diversity, equity, and accessibility in science in academia. The American Association for Anatomy (AAA), with its expressed and practiced culture of engagement, can serve as a model of best practice for other professional associations working to become more inclusive of individuals from historically underrepresented groups. In this publication, we acknowledge anatomy's exclusionary past, describe the present face of science in academia, and provide recommendations for societies, including the AAA, to accelerate change in academia. We are advocating for scientific societies to investigate inequities and revise practices for inclusivity; develop and empower underrepresented minority leadership; and commit resources in a sustained manner as an investment in underrepresented scientists who bring diverse perspectives and lived experiences to science in academia.
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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 | 0.167 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.036 | 0.051 |
| Open science | 0.011 | 0.031 |
| Research integrity | 0.065 | 0.065 |
| Insufficient payload (model declined to judge) | 0.020 | 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; 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".