Aligning police practice with hate crime theory: The case for using risk assessments to improve police response to victims of hate
Bibliographic record
Abstract
In England and Wales, police forces have been urged to improve their response to victims. Despite this, many victims continue not to report to police, and those that do often report distrust and dissatisfaction with police response. Across the hate crime strands, victims have little confidence in the capacity of police to act empathetically, to respond to hate crime effectively, or to take hate crime victimisation seriously. In this paper, we argue that risk assessments (RAs) represent a useful tool to bridge the gap between the reality of hate crime victimisation and current practice. We suggest that RAs – tools designed to assess a victim’s risk of potential future victimisation – can not only help the police to implement safeguarding but also provide a fuller understanding of the impact and harms of hate crime, so that police have a more holistic perspective. The use of RA may ensure that victim perspectives remain at the centre of police response.
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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 | 0.161 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".