Advancing Institutional Policies and Practices for Gender-Based Violence Prevention
Bibliographic record
Abstract
Over the past decade, universities and colleges have been under pressure to respond to incidents of gender-based and sexual violence (GBSV). In Ontario, Canada, the provincial government requires all postsecondary institutions to have standalone policies to address and resolve complaints. While universities within the province are not required to provide prevention education, it is common practice to include consent education during new student orientation. The effectiveness of such policies and educational practices in changing the culture of violence against women on campuses is questionable. In this chapter, we discuss the dissonance of culture, education, and response in addressing GBSV on campus. We reflect on the challenges students experience in applying the consent education they have received in real time, partly due to misunderstanding consent as a singular action rather than an ongoing process. We reflect on how a sustained conversation about sexual violence because of a number of high-profile national incidents has led to new approaches that transform gender-based violence prevention work with a view to supporting sexual agency. Central to this, we explain, was reframing sexual violence prevention work to focus on positive body image, healthy sexuality, pleasure, and positive relationships. Finally, we highlight the role that faculty and student leaders played in supporting the development and implementation of this new approach to gender-based violence prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".