Equity in action: a 4-year journey towards gender parity and racial diversity in biochemistry hiring
Bibliographic record
Abstract
Recruitment of faculty members in academic departments shapes the department for decades in research and teaching arenas. A diverse department is beneficial for all students as representation of underrepresented minority groups in the professoriate can inspire a greater diversity of students to pursue higher levels of education or research-focused careers. Increased diversity benefits research directly as diverse teams have been shown to have better ideas and outcomes. In 2020, our department had lower gender diversity than expected based on the pool of qualified personnel in Canada. Therefore, we altered our hiring process, primarily by redacting applications, for recruitment into entry-level tenure-track faculty positions. This resulted in the increased hiring of women (17% to 80%) with no substantial change in hiring of racially diverse individuals (50% to 40%). Overall, combined with retirements, the percentage of women faculty in the department went from 25% to 50% and the percentage of racialized faculty went from 38% to 44%. Thus, our intervention was successful in increasing the diversity of our department within a short timeframe. Our experience could provide other departments with a template for making substantive change, even in the absence of internal expertise in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".