Integrating Sexual Assault Resistance, Bystander, and Men’s Social Norms Strategies to Prevent Sexual Violence on College Campuses: A Call to Action
Bibliographic record
Abstract
Sexual assault prevention on college campuses often includes programming directed at men, women, and all students as potential bystanders. Problematically, specific types of sexual assault prevention are often implemented on campuses in isolation, and sexual assault risk reduction and resistance education programs for women are rarely integrated with other approaches. With increasing focus on the problem of sexual assault on college campuses, it is timely to envision a comprehensive and interconnected prevention approach. Implementing comprehensive prevention packages that draw upon the strengths of existing approaches is necessary to move toward the common goal of making college campuses safer for all students. Toward this goal, this commentary unpacks the models and mechanisms on which current college sexual assault prevention strategies are based with the goal of examining the ways that they can better intersect. The authors conclude with suggestions for envisioning a more synthesized approach to campus sexual assault prevention, which includes integrated administration of programs for women, men, and all students as potential bystanders on college campuses.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".