Sexual Violence at University: Are Indigenous Students More at Risk?
Bibliographic record
Abstract
University-based sexual violence prevalence is worryingly high and leads to many serious consequences for health and academic achievement. Although previous work has documented greater risk for sexual violence among Indigenous Peoples, little is known about university-based sexual violence experienced by Indigenous students. Using a large-scale study of university-based sexual violence in Canada, the current study aims to (1) examine the risk of sexual violence against Indigenous students compared to non-Indigenous students, and (2) to document sexual violence experiences of Indigenous students. Undergraduate students from six universities ( N = 5,627) completed online questionnaires regarding their experience and consequences of university-based sexual violence (e.g., forms of sexual violence experiences, gender, and status of the perpetrator, context of the violence, PTSD, disclosure). Findings indicated that compared with their non-Indigenous peers, Indigenous students experienced significantly higher levels of sexual harassment. However, no difference was found for unwanted sexual behaviors, nor for sexual violence contexts. Among Indigenous students, those having experienced sexual violence after age 18 (outside university) were more likely to report university-based sexual violence. Overall, findings highlight that Indigenous students, as well as non-Indigenous students, experience university-based sexual violence. Given their history, Indigenous students may have different needs, so sustainable policies that foster cultural safety on all campuses are clearly needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".