INSTITUTIONAL MEASURES TO PREVENT AND FIGHT AGAINST SEXUAL VIOLENCE IN UNIVERSITIES - THE CASE OF QUEBEC, CANADA
Bibliographic record
Abstract
The high prevalence of sexual violence within academic institutions has been internationally documented.Furthermore, the phenomenon of under-reporting of sexual violence experiences has been observed worldwide.Official complaints to universities reflect only a very small proportion of sexual violence acts experienced by community members.Thus, understanding how higher education institutions can support victims of sexual violence, including in their reporting process is needed to improve upon current practices.Our results from two consecutive studies conducted in Quebec offer promising measures to stimulate the reflection process of higher education institutions in the fight against sexual violence.Based on a sample of 9,234 students and employees, the first study revealed the high prevalence of campus sexual violence in Quebec.Over one in three individuals have experienced at least one situation of sexual violence and less than 10% of victims had reported to their university.The second study used a qualitative methodology to conduct interviews with 22 victims to explore their experiences of reporting to their home university.Analyses shed light onto central themes, in particular the obstacles identified by victims in their reporting process to the university.These obstacles can be related to structural elements specific to institutions (e.g., specialized services' accessibility, sexual and gender-based policy) and the responses they can provide to victims, as well as elements belonging to the victim's environment and personal characteristics.By adopting actionable measures centered around the needs of victims, higher education institutions can promote a healthy and safe environment for community members, free of all forms of violence.The actual and sustained mobilization of institutional leaders and stakeholders in the fight against gender-based and sexual violence is an essential condition for cultural change in universities, and in so doing, would contribute to an equitable access to education within a social justice context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".