Racial Profiling in Canada: Challenging the Myth of "A Few Bad Apples."
Bibliographic record
Abstract
Racial Profiling in Canada: Challenging the Myth of Few Bad Apples. Carol Tator and Frances Henry. Toronto: University of Toronto Press, 2006. 251 pp. $75.00 hc; $35.00 sc. Tator and Henry's book was apparently prompted by a series of articles published in the Toronto Star newspaper in October 2002 about the repeated stopping and searching of racial minority individuals, especially young African Canadians. The Star series generated a heated debate over this issue between the Toronto police authorities, authors of the newspaper articles (and the Star as an institution), and local authorities. The Toronto Police Association contested the newspaper articles' validity and denied any systemic application of racial profiling by police officers. The general purpose of the book, the authors state, to uncover and deconstruct racial profiling practices in Canadian society (p. 17). They have done so using a multidisciplinary, discursive approach in which they examine the meaning and implications of facial profiling in theory and practice. The authors make bold statements about racism in Canada. Historically, they refer to the treatment of Aboriginals, slaves, and later the Japanese; and currently, they point to the racist practices that target various racial and ethnic minorities. Racism, they argue, flourished to this day (p. 39) and has always been institutionalized in politics, law, education, and the media (pp. 187-88). Ironically, we must note that it was the Star's media reports that led to the writing of this book. Theoretically and methodologically, the book is inspired by the post-structuralist and post-modernist approaches of Michel Foucault and Pierre Bourdieu, among others, which give local and micronarratives a prominent role in the production of knowledge to counter the metanarratives of the dominant groups and classes. Yet the book's methodology combines quantitative and qualitative data collection techniques (provided by two contributory authors in two separate chapters) to assess the extent of racial profiling and document, through interviews, the feelings and reactions of those subjected to it. In the other six chapters, Tator and Henry analyze relevant concepts and theories and discuss the importance of narration in understanding the realities of racial profiling. The authors argue that racial profiling is a manifestation of democratic racism, in which bias and discrimination cloak their presence in liberal principles. The white majority uses a racialized discourse as a strategy to tutu attention away from racial profiling as a concrete social problem. …
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.056 | 0.040 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".