Bibliographic record
Abstract
Despite a very sophisticated and rich jurisprudence on racial profiling, there are very few criminal cases in Canada where the issue has been litigated. This is as true today in 2016 as it was in 2006 when I wrote this article examining cases from 2003-2006. This piece from 2006 explores why there is such litigation silence. It also develops arguments about how race and systemic racism are relevant in thinking about the meaning of detention under section 9 of the Charter and in the interpretation of behaviour that the police often believe gives rise to the necessary reasonable suspicion to conduct an investigative detention. Finally, the piece identifies the relevance of the failure of the police to collect race data on street interactions in thinking about admissibility under section 24(2) of the Charter. Postscript: In 2009, the Supreme Court of Canada dismissed an appeal in R v Grant 2009 SCC 32, one of the cases discussed in this article. While the Court recognized the relevance of minority status to the question of detention, the majority opinion did not address the issue in discussing whether or not Grant was detained. Nor did it address the broader issue of racial profiling or its relevance in thinking about whether the evidence should be excluded under section 24(2) of the Charter.
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 | 0.032 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".