Responding to Sexual Assault on Campus: What Can Canadian Universities Learn from US Law and Policy?
Bibliographic record
Abstract
Our starting point is that universities should provide avenues of redress for women who experience sexual violence and that these cannot simply be absorbed into pre-existing disciplinary codes and sexual harassment policies. Canadian governments have the power to impose uniform reporting and disciplinary procedures on universities, but in the absence of national or provincial standards, best practices should be identified for such policies. We first turn to a brief discussion of the legal context in which Canadian post-secondary institutions operate, particularly federalism, provincial human rights codes, the Charter of Rights and Freedoms, and tort law. Second we describe the legal context in which US universities and colleges sit: Title IX, the Clery Act, the Obama Task Force and its 2014 Report, and the ongoing investigations and litigation arising from federal regulation. Third we look at what Canadian institutions might learn from the US experience specifically on the issues around reporting obligations, disciplinary measures, and protections for women who report sexual violence.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".