A Qualitative Examination of the Impacts of Police Practices on Racialized and Marginalized Young People in Toronto
Bibliographic record
Abstract
Canada enjoys an international reputation for tolerance, diversity, and inclusivity. However, a closer examination reveals Canada’s long-standing history of systemic racism, including in its public-sectors organizations and, most notably, the criminal justice system. In particular, Black people are among the primary recipients of disparate treatments at the hands of the police. However, while these issues are well-studied in the United States and United Kingdom, there remains a lack of Canadian scholarship examining the intersections of race and the criminal justice system. My dissertation seeks to better inform the discourse around these issues through an examination of the lived experiences of young people in Toronto, Ontario with policing, violence, and community safety. Drawing on 42 in-depth, semi-structured interviews with young people, ages 16-29, my study explores how aggressive, order-maintenance policing, economic marginalization, and geographic segregation have impacted young people’s perceptions of the police, the criminal justice system, and Canadian society. In doing so, I engage with dominant theoretical perspectives related to urban policing, including procedural justice and legitimacy, subcultural violence, legal cynicism, and social disorganization. My findings explore several important themes, including the contours of long-standing rivalries between non-gang youth who reside lower-income communities or ‘opp blocks’ (Chapter 2); the salience of anti-snitching discourses among young people and the relationship between perceptions of police legitimacy and efficacy and willingness to comply with police investigations (Chapter 3); and the collective impacts of police practices through a comparison between Black and white youths, including the role of direct and vicarious contacts in shaping perceptions of the police (Chapter 4). Taken together, my findings demonstrate the pervasive and wide-spread impacts of proactive policing practices that have disproportionately targeted Black youths and Black communities, including diminished perceptions of the police, reduced social and spatial mobility, and involvement various self-help behaviours, including private violence. I conclude by discussing the theoretical and policy implications of my findings, which are particularly salient as Toronto continues to face mounting levels of youth violence, racialized poverty, and spatial segregation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".