Racial and Ethnic Profiling: Statutory Discretion, Constitutional Remedies, and Democratic Accountability
Bibliographic record
Abstract
Given the prominence of the issue of racial, ethnic, and religious profiling in the public debate about terrorism, it is significant that Canada's two legislative responses to September 11 - the Anti-terrorism Act and the proposed Public Safety Act - are silent on the issue, neither explicitly authorizing profiling nor expressly banning it. In this article, we focus on the constitutional remedies available for profiling in the face of these statutory silences, and the implication that the choice of remedies holds for both remedial efficacy and democratic accountability. Contrary to the position held by the majority of the Supreme Court in Little Sisters v. Canada, we argue that if profiling were to take place pursuant to an exercise of statutory discretion, the statute itself should be constitutionally challenged and struck down because the infringement of the right to equality is not "prescribed by law." The result would be to force the issue of profiling back onto the legislative and democratic agenda. By contrast, focusing the challenge on the exercise of discretion would trigger remedies under section 24 that would be largely ineffective and retrospective, which would not trigger democratic debate.
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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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.050 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".