Using the Charter to Stop Racial Profiling: The Development of an Equality-Based Conception of Arbitrary Detention
Bibliographic record
Abstract
Do the police use race as a proxy for criminality, particularly, in drug cases? If so, is this a rational discriminatory practice that is based on who the usual offender is or an offensive exercise of racial prejudice? What are the consequences for those communities targeted by the police? This article investigates these questions that have gone unanswered for too long in Canada. After offering a definition of racial profiling, evidence is presented that suggests that the practice is rampant in the United States and is likely practiced by some Canadian police forces, particularly, in cities with large visible minority populations. As for its rationality, recent statistical evidence on drug use and trafficking reveals that racial profiling is a fallacy. As for its reasonableness, racial profiling has had a catastrophic impact on those communities targeted by the police. This article examines how the Charter can be used to stop this practice. Since racial profiling is exercised through the use of pretext vehicle stops and investigative detentions, the focus is on section 9 of the Charter which protects against arbitrary or discriminatory police detentions. While the seminal section 9 cases of Brown v. Durham Regional Police and R. v. Simpson provide some protection against racial profiling, issues of proof and cognitive distortion limit their effectiveness. Thus, enhanced section 9 standards need to be developed. This article looks at infusing section 9 with the equality principles animating section 15(1) of the Charter.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".