Bibliographic record
Abstract
In the landmark Canadian racial profiling case of R v Brown, an unanimous Ontario Court of Appeal firmly recognized that racial profiling is a reality that is “supported by significant social science research.” Brown established a correspondence test for proving racial profiling. This paper aims to set out, in some detail, how and when the correspondence test can be applied. Part I sets out the test from Brown. Part II identifies the different manifestations of racial profiling. Part III examines the relevant indicators that can be used to meet the test. These indicators include context, pretext and lessons learned. Part III also summarizes the recent carding/street check data which reveals the widespread nature of the disproportionate policing of Black and other racialized individuals in a number of cities across Canada. It is suggested that this evidence requires a reconsideration of the argument made in Peart v Peel Regional Police Services that there should be a rebuttable presumption of racial profiling in litigation. Parts II and III are presented in a largely non-traditional format to enhance accessibility and appreciation of the nature and scope of the problem. The paper concludes with a discussion of the relevance of the impact of racial profiling in assessing whether to exclude evidence found in breach of the Charter even where there is no finding of racial profiling in the particular case. This is an important contribution to our exclusionary rule jurisprudence and should be relied on in any case involving a racialized or Aboriginal accused. Finally, an Appendix is included which documents twenty-eight (28) positive judicial and tribunal findings of racial profiling by police in Canada in the post-Charter era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".