Disambiguating the Wrongs of Racial Profiling in Policing and Championing Their Structural Remediation: A Reply
Bibliographic record
Abstract
CANADIAN LAW JOURNALS have never been known for going out of their way to facilitate direct conversations between legal scholars working on cognate issues. However, if there is anything to the old liberal adage that the truth is more likely to emerge from the civil yet robust debating of competing ideas, the lack of opportunities for holders of rival views to respond, in real time, to each other’s arguments is deplorable. Therefore, I wish to commend the Osgoode Hall Law Journal for convening this timely scholarly exchange on the problematic phenomenon of racial profiling in Canadian policing and, more specifically, the under-explored question of how courts should respond to it in view of Canada’s distinct legal framework. I also wish to thank Terry Skolnik, Fernando Belton, and Jeanne Mayrand-Thibert (hereinafter SBMT) for agreeing to engage in this dialogue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".