Bibliographic record
Abstract
Abstract In the midst of unprecedented attention to gender-based violence (GBV) globally, prompted in part by the #MeToo movement, this book provides a new analysis of how higher education cultures can be transformed. It offers reflections from faculty, staff, and students about how change has happened and could happen on their campuses in ways that go beyond implementation of programs and policies. Building on what is already known from decades of scholarship and practice in the United States, and more recent attention elsewhere, this book provides an interdisciplinary, international overview of attempts to transform higher education cultures to eradicate GBV. Change happens because people act, usually with others. At the heart of transformative efforts lie collaborations between faculty, staff, students, activists, and community organizations. The contributors to the book reflect on what makes for constructive, effective collaborations and how to avoid the common mistakes in working with others to end GBV. They consider what has worked to challenge the reluctance—or outright hostility—they have encountered in their work against GBV and how their collaborations have succeeded in transforming the ways GBV is considered and dealt with. The chapters focus on experiences in Canada, the United States, England, Scotland, France, and India to examine different approaches to tackling GBV in higher education. They reveal the cultural variations in which GBV occurs as well as the similarities across cultures. Together, they demonstrate that, to make higher education a safe environment for all, nothing short of a transformation is required.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".