Keynote Presentation - Reducing sexual violence: From pilot research to international scale-up in 15 years
Bibliographic record
Abstract
Dr. Charlene Y. Senn is a Professor of Psychology and Women’s and Gender Studies and Tier 1 Canada Research Chair in Sexual Violence at the University of Windsor. She is an expert on effective sexual violence interventions, particularly those developing women’s capacity to resist sexual assault. She created the Enhanced Assess, Acknowledge, Act sexual assault resistance education program for women in the first year of university. Findings from the randomized controlled trial evaluation were published in 2015 in the New England Journal of Medicine. This 12-hr program resulted in a 46% reduction in completed rapes and 63% reduction in attempted rape experienced across one year, when compared with the control group. The program accomplishes this while reducing woman-blaming and self-blame.\nWith her co-investigators, Charlene is currently conducting a study of implementation and effectiveness of the program as it is offered at Canadian universities. In 2016, she created a non-profit (SARE Centre) to facilitate scale-up on campuses around the world. In this presentation, Charlene will explore her journey from small internally funded pilot studies, to increasingly more complex CIHR-funded studies, to “real world” implementation.
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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 | 0.047 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".