Exploring the Dark Figure of Hate: Experiences with Police Bias and the Under-reporting of Hate Crime
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hate crimes are notoriously under-reported, and the present research explores possible etiological factors for this phenomenon in a national Canadian sample. Controlling for demographic and offence characteristics, the research shows that victims who had prior experiences with police discrimination were significantly less likely to report hate crimes to police compared to victims of non-hate-based crimes. Additionally, victims experienced hate crimes in a more intersectional way than is typically reflected in police reports, as victims tended to interpret the offence as targeting multiple overlapping identities rather than a solitary hate motivation. These findings reflect the unique nature of hate-motivated offences, whose victims may find it futile to report bias-motivated offences to a police force whom they believe is itself biased. These results suggest possible opportunities to improve reporting of offences and relations between police and marginalized communities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it