Studying Sexual and Gendered Violence Prevention in Higher Education: Local/Vertical, Global/Horizontal, and Power-Based Frames
Bibliographic record
Abstract
Les recents travaux sur les programmes de prevention de la violence sexuelle et sexiste dans les campus des colleges et universitaires a travers le monde ont revele le peu d’efficacite de ces initiatives qui sont plutot des pratiques qui l’interdisent et dont les concepts sont ignores par les erudits du milieu. Cet article explore avec l’aide de cadres analytiques bases sur le pouvoir et ancres dans la theorie et les pratiques feministes transnationales, utilise trois scenarios. Le premier rapporte les notes d’un groupe de chercheurs qui ont collige les donnees sur la violence sexuelle dans une grande universite publique de Bulgarie et examine comment les recherches selon une methode de l’Union europeenne, selon une ong internationale feminine et des approches preventives et imaginatives americaines furent appliquees a l’ epistemologie bulgare. Le deuxieme scenario questionne les actions contradictoires des universites responsables de la violence envers les femmes et autres participants une education superieure tout en les protegeant contre cette violence. Le troisieme scenario suggere des programmes de prevention envers une population specifique d’etudiants, dans ce cas, envers les cinq millions d’etudiants qui transitent dans le monde de l’education et qui sont invisibles dans le champ de la prevention.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".