Creating a Rebuttable Presumption of Profiling in Cases of Alleged Profiling of Muslims and Other Minorities in Canada
Bibliographic record
Abstract
This paper addresses the subject of profiling in the Canadian context both in the narrower and broader senses. It discusses the close connection between wider discretionary power and profiling; the need and justification of rebuttable presumptions and its constitutional basis and discusses possible ways to end profiling in line with the contextual analysis of equality under s. 15 of the Canadian Charter of Rights and Freedoms. The paper proposes for the shift in the burden of proof away from a victim of profiling to the law enforcement authority—in an alleged case of profiling—in the context of a search, or an arrest, of a Canadian Muslim or member of other minorities by police and other law enforcement authorities, and demonstrating that such search or arrest was not motivated by race, religion, or ethnicity. Thus creating a rebuttable presumption of profiling against the law enforcement authority when they use their discretionary power in search or arrest of a member of a racial, religious, or ethnic minority group. A member of Canadian law enforcement authority against whom profiling is alleged can rebut the presumption by proving that race, religion, or ethnicity was not a factor in using their discretionary power to stop, search, or detain the alleged victim of profiling.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".