Inflammatory Rhetoric? Baseless Accusations? A Response to Gabor's Critique of Racial Profiling Research in Canada
Bibliographic record
Abstract
Racial profiling has become one of the most controversial issues facing the Canadian criminal justice system. In a recent article, Thomas Gabor dismisses much of the evidence of racial profiling in Canada as "baseless" and "inflammatory." We address Gabor's critique by highlighting findings from a Toronto survey which suggest that black youth are much more likely to report being stopped and searched by the police than youth from other racial backgrounds. Logistic regression analysis reveals that the impact of race remains strongly significant after controlling for social class, self-reported criminal activity, gang membership, drug and alcohol use, and public leisure activities. The article concludes with a discussion of the impact that racial profiling has on minority communities and a brief review of the potential benefits - and consequences - of mandating the police to collect their own stop and search data.
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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 | 0.043 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.043 | 0.069 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 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".