Black Males' Perceptions of and Experiences with the Police in Toronto
Bibliographic record
Abstract
Canada is commonly depicted as a diverse and tolerant immigrant-receiving nation, accepting of individuals of various racial, ethnic, and religious backgrounds. Nevertheless, Canadian institutions have not been immune to allegations of racial bias and discrimination. For the past several decades, Toronto's Black communities have directed allegations of racial discrimination at the police services operating within the city. Using a mixed-methods approach, this thesis examines Black males' perceptions of and experiences with the police in the Greater Toronto Area. In order to provide a comprehensive examination of this issue, this thesis is comprised of three studies with three distinct groups of Black males. The first of these three studies utilizes data from a representative sample of Black, Chinese, and White adults from the Greater Toronto area to examine racial and gender differences in perceptions of and experiences with the police. The second study draws on data from a sample of young Black men recruited from four of Toronto's most disadvantaged and high crime neighbourhoods to examine the views and experiences of those most targeted by the police. The final study involves interviews with Black male police officers in order to draw on the perspectives of those entrusted with enforcing the law. In line with a mixed-model hypothesis, the findings suggest that Black males' tenuous relationship with the police is a product of their increased involvement in crime, as well as racism on the part of police officers and police services. Using insights drawn from Critical Race Theory, I suggest that both the increased levels of crime and the current manifestations of racism have a common origin in Canada's colonial past.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".