Reinforcing Safety or Perpetuating Harm? Examining The Role and Effectiveness of Bill 151 and SexualViolence Prevention Efforts in Montreal Universities
Bibliographic record
Abstract
This thesis examines the effectiveness of Bill 151 and sexual violence prevention efforts within Montreal universities. Bill 151, enacted by the Quebec government in 2017, mandates higher education institutions to implement policies, training, and reporting mechanisms to combat sexual violence. Through secondary data analysis, this research evaluates annual reports and prevention policies from seven Montreal universities, assessing their adherence to Bill 151 and their impact on reducing sexual violence. The study reveals inconsistencies in policy implementation, reporting quality, and prevention efforts among institutions. While some universities demonstrate transparency and thoroughness, others exhibit gaps in data collection, accountability, and survivor support. Findings indicate that institutional bias, lack of standardized reporting, and low participation in training programs hinder the effectiveness of these measures. Moreover, the persistence of underreporting and cultural barriers reflects systemic issues within academic environments. By analyzing trends in reported cases and prevention initiatives, the thesis underscores the need for intersectional, survivor-centered approaches that address the root causes of sexual violence and institutional shortcomings. Recommendations include enhancing training programs, improving data transparency, and fostering a cultural shift to combat rape culture and promote accountability. This research contributes to understanding how universities can better ensure campus safety and equity for all students.
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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 | 0.023 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".