Bibliographic record
Abstract
As a reflection of the iMPACTS Project, an international research partnership that investigates sexual violence at universities and in society, this edited collection is the first to take a multi-disciplinary approach to understanding and addressing sexual violence and gender-based violence in Canada. The first section of the book examines law/policy issues impacting universities, while the second section explores student activism and university responses to students’ experiences of sexual violence. The third section examines sexual violence interventions through education and pedagogy, including an arts-based toolkit, a theatre production, and an international internship program. The fourth and final section focuses on vulnerable communities, including online and in person as well as within legal, human rights, and social justice frameworks. While law and education are two major themes in this book, systematic and institutional discrimination are also examined. As such, this book emphasizes intersectional identities and the disproportionate effects of sexual and gender-based violence on marginalized communities. This book addresses policy makers, educators, students, workshop facilitators, archivists, theatre professionals, and members of the general public. This book could be recommended reading in university level courses across a range of subject areas including law, policy, education, gender studies, health, and sociology. This edited collection is unique in that it is focuses on the Canadian context and consolidates emerging research on sexual violence from a variety of disciplines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".