“Head of the Class”: equity discourses related to department head appointments at one Canadian medical school
Bibliographic record
Abstract
Purpose: Equitable appointments of departmental leaders in medical schools have lagged behind other Equity, Diversity, and Inclusion (EDI) advancements. The purpose of this research was to 1) analyze how policy documents communicate changing ideas of EDI, employment equity, and departmental leadership; and 2) investigate department heads’ perspectives on EDI policies and practices. Methods: We conducted a critical discourse analysis to examine underlying assumptions shaping EDI and departmental leadership in one Canadian medical school. We created and analyzed a textual archive of EDI documents (n = 17, 107 pages) and in-depth interviews with past (n = 6) and current (n = 12) department heads (830 minutes; 177 pages). Results: Documents framed EDI as: a legal requirement; an aspiration; and historical reparation. In interviews, participants framed EDI as: affirmative action; relationships; numerical representation; and relinquishing privilege. We noted inconsistent definitions of equity-deserving groups. Conclusions: Change is slowly happening, with emerging awareness of white privilege, allyship, co-conspiracy, and the minority tax. However, there is more urgent work to be done. This work requires an intersectional lens. Centering the voices, and taking cues from, equity-deserving leaders and scholars, will help ensure that EDI pathways, such as those used to cultivate department leaders, are more inclusive, effective, and aligned with intentions.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.063 | 0.034 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".