The Chilly Climate for Women Judges: Reflections on the Backlash from the Ewanchuk Case
Bibliographic record
Abstract
The public furor that accompanied the release of Justice Claire L'Heureux-Dube's decision in R. v. in 1999 was both extreme and unprecedented. This article analyzes the melee as a case study of the response of the legal community and the media to judges who are perceived as feminist. Drawing upon the chilly climate literature that has documented the problems faced by women in academia, it explores the environmental discrimination that makes it difficult for women judges, particularly feminists, to adjudicate on an equal basis with male judges.Le tolle qui a accompagne la publication de la decision de Madame la juge Claire L'Heureux-Dube dans l'arret R. c. en 1999 etait a la fois extreme et sans precedent. Le present article analyse l'affaire Ewanchuk comme un cas type de la reaction de la communaute juridique et des medias aux juges qui sont percues comme feministes. En puisant dans la documentation sur les problemes profonds auxquels sont confrontees les femmes dans le monde universitaire, regroupee sous l'expression du climat glacial, l'article explore la discrimination du milieu qui rend docile pour les femmes juges, en particulier les feministes, la tâche de rendre jugement sur un pied d'egalite avec les juges masculins.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.060 | 0.034 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".