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Record W289021946

Racial Profiling in Canada: Challenging the Myth of "A Few Bad Apples."

2006· article· en· W289021946 on OpenAlexvenueaboutno aff
Omar Chaoura Bourouh

Bibliographic record

VenueCanadian ethnic studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsRacial profilingNewspaperRacismRacial politicsMythologyPoliticsSociologyProfiling (computer programming)Media studiesLawCriminologyGender studiesHistoryPolitical scienceClassicsRace (biology)
DOInot available

Abstract

fetched live from OpenAlex

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. …

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.135
Threshold uncertainty score0.982

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0560.040
Scholarly communication0.0200.010
Open science0.0040.006
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.183
GPT teacher head0.413
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations107
Published2006
Admission routes2
Has abstractyes

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