Doing Away with Racial Profiling in Policing Without Doing Away with the Rule of Law
Bibliographic record
Abstract
Since the turn of the millennium, Canadian appellate courts have been investing increasingly systematic efforts in demystifying and curtailing racial profiling in policing. These judicial efforts have so far been focused on the application of the legal criteria for arrest and detention as well as their regulation under section 9 of the Canadian Charter of Rights and Freedoms. In this article, I contend that this unidimensional approach is unsound and outline a corrective path forward. First, I argue that the prevailing judicial understanding of what racial profiling is and how it affects the lawfulness of arrests and detentions has the paradoxical effect of undercutting the rule of law, the advancement of which is the very purpose of section 9. It chiefly does so by requiring an overbroad range of arrests and detentions to be declared unlawful. Second, I contend that the current approach also fails to address racial profiling for the core wrong that it constitutes—namely, wrongful discrimination on the ground of race, which section 15 of the Charter expressly prohibits. I make the case that addressing the phenomenon under this complementary paradigm would make it possible for courts to censure and remedy arrests and detentions tainted by it even when, for rule-of-law-related reasons, they should not be declared unlawful. Thus, it would offer courts the ability to thread a more careful and complementary remedial needle. Finally, I raise the possibility of a third paradigm—that of judicial stays of proceedings for abuses of process—to help address cases that the other two paradigms are ill-suited to redress.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.067 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".