JUDGING SEXUAL ASSAULT: THE SHIFTING LANDSCAPE OF JUDICIAL EDUCATION IN CANADA
Bibliographic record
Abstract
The authors, a law professor and a former judge, examine recent events that raise critical questions about judicial education on sexual assault. These include the Inquiry into the conduct of Justice Robin Camp, the consequent unanimous passing by Parliament of Bill C-337, the Judicial Accountability through Sexual Assault Law Training Act, and the institutional response of the Canadian Judicial Council (CJC) to the public outcry and the political response. The article considers the tension between the legitimate call for judicial accountability and the equally legitimate desire to protect judicial independence at a time of increasing public and political awareness of entrenched female inequality and its relationship to male sexual violence. They argue that the judicial education provisions of Bill C-337 reflect this tension and, as a result, are likely to be ineffective if proclaimed in force. While they identify many positive aspects to the CJC response to the Bill, the authors conclude that the CJC’s continued insistence that judicial education be controlled, supervised, and implemented by judges is inadequate. They suggest that a respectful, continuous, and dynamic collaboration among judges, legal and other academics, and community members with relevant experience and expertise will contribute to public understanding of the judicial role at the same time as it increases the likelihood that judicial education will enhance women’s equal treatment in the nation’s courtrooms.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".