Bibliographic record
Abstract
Racial profiling in law enforcement is a contentious matter, particularly in light of U.S. police-citizen race tensions. The racial profiling debate has not been settled. Racial profiling proponents view it as a tool to effectively uncover criminal activity among certain racial groups. Critics find that racial profiling perpetuates racial stigmas and is largely inefficient as a policing tool. This article explores the ongoing debate and offers an overview of the Canadian judicial experience with racial profiling. The author proposes a middle-ground solution where racial profiling may be used under certain constraints imposed on law enforcement. The author suggests that the Crown provide justificatory evidence for the use of racial profiling when it is raised as a defence by the accused.\nThis article is helpful for readers seeking to learn more about: racial profiling, race-based profiling, driving while Black, police oversight, police accountability \nTopics in this article include: race, racism, racial stigmas, police, policing tools, law enforcement, evidentiary burden, policing tool, and discrimination, Canada, United States, Tupac Shakur, Donald Marshall Jr, Neil Stonechild, Aboriginals, Vietnamese, Punjabis, South Asians, police officers, marijuana, traffic stop, traffic stops \nAuthorities cited in this article includes: Canadian Charter of Rights and Freedoms Stonechild Inquiry Royal Commission on the Donald Marshall Jr. Prosecution R v Khan (2004), 244 DLR (4th) (Ont Sup Ct) R v Mann 2004 SCC 52.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".