Is Gender Parity Imminent in the Professiorate? Lessons from One Canadian University
Bibliographic record
Abstract
This article examined issues and implications associated with gender parity in the professoriate. The findings, based on the results from one Canadian institution’s most recent women’s committee report, emphasize the importance of monitoring progress toward gender parity by examining potential indicators of gender imbalances such as gender differences in applicant pools, starting rank and salary, and promotion applic‐ ation and attainment. This article addresses implications for recruitment, hiring, and formalized reporting mechanisms that can contribute to ultimately attaining gender parity in academia. Key words: post‐secondary education, faculty hiring, status of women Cet article porte sur les problèmes et les incidences liés à la parité hommes‐femmes dans le corps professoral universitaire. Les conclusions des auteures, basées sur les résultats d’un rapport récent préparé par un comité de femmes dans une université canadienne, soulignent l’importance de suivre l’évolution de la question de la parité hommes‐femmes en étudiant les indicateurs potentiels des déséquilibres en la matière, notamment les écarts entre le nombre de femmes et d’hommes dans les bassins de candidats, le salaire et le rang de départ ainsi que les demandes de promotion et leur obtention. Cet article traite notamment des procédures de communication d’information sur le recrutement et l’embauche pouvant favoriser la parité hommes‐femmes dans les universités. Mots clés : éducation postsecondaire, embauche des membres du corps professoral, statut des femmes
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".