"We Expect Much of You": Enlisting Youth in the Policing of Marginalized Communities
Bibliographic record
Abstract
In this paper, we examine a police summer program for youth from marginalized communities designed to both provide valuable experiences and opportunities as well as improve the problematic relationship between marginalized youth and police. Using the concepts of racialization, governance, and interest-convergence, we consider how such institutionally sponsored programs socialize youth into a value system that fosters self-governance and restraint, particularly in the case of youth who, because of where they live, are considered to be "at risk." Hence, they are subjected to interventions that operate in subtle ways to regulate them into what are deemed appropriate values and beliefs. We found that after six weeks of working with the police, the attitudes of the youth changed from avoidance to enthusiastic endorsements of police practices. In this way, the program worked to advance the institutional interests of the police without the organization having to confront or adjust any of its own practices and assumptions. Ultimately, the program, with its focus on attitudes, behaviour, and opportunity, promotes and achieves the kind of reform that never questions structural and systemic inequalities. Dans cet article, nous examinons un programme estival organisé par la police de Toronto pour les jeunes de communautés marginalisées, conçu à la fois pour leur offrir des occasions et des expériences utiles et pour améliorer la relation problématique qu'ils ont avec la force publique. À partir des concepts de racialisation, de gouvernance et de convergence d'intérêts, nous constatons comment ces programmes, parrainés par les institutions, les socialisent en leur instillant un système de valeur qui les encourage à se prendre en mains et à se contrôler, surtout dans le cas de ceux qui, en raison de leur lieu de résidence, sont considérés comme « à risque ». En conséquence, ces jeunes sont soumis à des interventions qui fonctionnent de manière subtile afin de les convaincre d'adopter de bonnes valeurs, ou ce qui est jugé comme tel. Nous constatons qu'au bout de 6 semaines de travail avec la police, leur attitude a changé : ils passent de l'évitement à une approbation enthousiaste des pratiques policières. Le programme a fait ainsi progresser les intérêts institutionnels des forces de l'ordre sans que l'organisation ait eu à se remettre en question ou à ajuster ses propres pratiques et méthodes. Finalement, en mettant l'accent sur les attitudes, le comportement et les possibilités qui leur sont offertes, il favorise et obtient un type de réforme qui ne questionne jamais les inégalités du système et des structures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".