Bibliographic record
Abstract
Police officers??? perceptions of gender-motivated violence against women have been\noverlooked in hate crime research. In an attempt to fill a gap in the hate crime, violence against\nwomen, and policing hate crime literature, I examine how nine police officers understand\ngender-motivated violence in Canada using vignettes, sentence-competition tasks, and an\ninterview guide. Here, participants are asked about their perceptions of and experience with hate\ncrime and gender-motivated hate crime against women. Results indicate that the majority of\nparticipants do not perceive hypothetical instance of violence against women as hate crime, all of\nwhich is a product of: victim-perpetrator relationships, ambiguous motives and alternative\nmotives, and definitional constraints with legal terms. Equally, factors and conditions that\ninfluence police officers??? perceptions relate to: the typical victims of hate notion, police routine\nand experience with hate crime and gender-motivated violence, hate crime legislation, hate crime\npolicies and procedures for police, and hate crime training for police.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".