A sponsorship action plan for increasing diversity in STEMM
Bibliographic record
Abstract
There are numerous structural and cultural barriers to the progression of women and marginalized groups to leadership in academia, especially in Science, Technology, Engineering, Mathematics and Medicine (STEMM). A range of interventions have been described to address this inequity, with varying success. Here, we suggest that sponsorship could be one effective intervention and propose an institutional action plan to implement a sponsorship program in academia. We outline why sponsorship could be an effective strategy, especially if implemented through a deliberate program by an institution. We then detail the three components of an action plan to be considered in implementation: the elements of the program, the activities that sponsorship in academia likely encompasses, and the selection of sponsors and protégés. The plan could also be enacted by academic leadership in the absence of an institutional program and could serve as a guide to individuals in academia aspiring to address diversity and inclusion in STEMM.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".