Bibliographic record
Abstract
In October 2002, the Toronto Star ran a series of feature articles on racial profiling in which it was indicated that Toronto police routinely target young Black men when making traffic stops.The series drew strong reactions from the community, and considerable protest from the media, politicians, law enforcement officials, and other public figures.Although the articles were supported by substantial documentation and statistical evidence, the Toronto Police Association sued the Star, claiming that no such evidence existed.The lawsuit was ultimately rejected in court.However, as a result, the issue of racial profiling -a practice in which certain criminal activities are attributed to individuals or groups on the basis of race or ethno-racial background -was thrust into the national spotlight.In this comprehensive and thought-provoking work, Carol Tator and Frances Henry explore the meaning of racial profiling in Canada as it is practised not only by the police but also by many other social institutions.While providing a theoretical framework within which they examine racial profiling from a number of perspectives and in a variety of situations, the authors analyse the discourses of the media, policing officials, politicians, civil servants, judges, and other public authorities to demonstrate how those in power communicate and produce existing racialized ideologies and social relations of inequality through their common interactions.Chapter 3, by contributing author Charles Smith, provides a comparison of experiences of racial profiling and policing in Canada, the United States, and the United Kingdom.Chapter 7, by Maureen Brown, through a series of interviews, presents stories that demonstrate the realities of racial profiling in the everyday experiences of Afro-Canadians and ethno-racial minorities.Informed by a wealth of research and theoretical approaches from a wide range of disciplines, Racial Profiling in Canada makes a major contribution to the literature and debates on a topic of growing concern.Together the authors present a compelling examination of the pervasiveness of racial profiling in daily life and its impact on our society, while suggesting directions for change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.768 | 0.546 |
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".