Exploring racial profiling as a policing and human rights problem in Toronto
Bibliographic record
Abstract
This paper explores racial profiling as a policing and human rights problem in Toronto. The rationale behind this paper is founded on the racial equity protests and emotional outburst of the Black youth in Toronto expressing decades of police brutalities, fear, pain and grief from legacies of slavery and colonialism. The youth’s views and the historical relations of race are critical to people-police relations in fostering trust and collaboration amid the struggles of racial profiling. To accomplish this, I embarked on review of secondary sources by consulting current literature on racial profiling by the Toronto Police Service. I also analysed different sources such as, government archives, books, policy data, journals and newspapers. I also integrated knowledge gained through my practicum experience, especially investigative skills, reading and summarizing case files at the Manitoba Human Rights Commission. This is not an exhaustive literature about the racial profiling of the Black People in Toronto by the Toronto Police Service. However, the content of the paper represents my research and contributions. Based on the literature reviewed, I concluded that: (1) Racial profiling is borne out of the systemic racism that has ridden every fabric of the Canadian society, (2) Racial profiling is more prevalent among Afro-Canadians than other racialized youths, (3) Black youths are disconnected from the Toronto Police Service and government is swamped with piles of unimplemented policies that would have aided youth collaborations and synergy with the police, (4) The role of government and launched police initiatives are not sufficient, which begs for more research and advocacy on racial profiling of racialized communities in Toronto. The recommendations include ideas for inclusion, integration and changes to policy makers and the police.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.047 | 0.022 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".