“It Just Makes You Have More Problems”: An Examination of Anti-snitching Codes among Black Youths in Toronto
Bibliographic record
Abstract
Subcultural codes against compliance with the police, or “snitching,” have factored prominently in public and law enforcement discourses related to urban violence and crime prevention. However, scholarship on these issues focuses almost entirely on the United States. This study investigates attitudes toward compliance with the police and perceptions of snitching among a sample of a Black youths who reside in socially and economically marginalized neighbourhoods in Toronto. Drawing on 32 in-depth interviews, I examine how perceptions of community safety and experiences with policing have impacted young people’s willingness to report crimes and comply with police investigations. Contrary to popular discourses, being seen speaking with police or providing information did not necessarily constitute snitching. Rather, consistent with prior research, a complex set of variables, including age, gender, and the perceived seriousness of the crime, all factored in determining what constituted snitching and when someone was considered a snitch. My findings challenge the essentializing nature of popular discourses on snitching while also highlighting how diminished perceptions of police legitimacy and efficacy have impacted young people’s willingness to report crimes and comply with police investigations. Finally, I discuss the implications of my findings for efforts to reform the police and improve police–community relations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".