Bibliographic record
Abstract
Starting in the 1930s, urban police forces from New York City to Montreal to Vancouver established youth squads and crime prevention programs, dramatically changing the nature of contact between cops and kids. Gone was the beat officer who scared children and threatened youth. Instead, a new breed of officer emerged whose intentions were explicit: befriend the rising generation. Good intentions, however, produced paradoxical results. In Youth Squad Tamara Gene Myers chronicles the development of youth consciousness among North American police departments. Myers shows that a new comprehensive strategy for crime prevention was predicated on the idea that criminals are not born but made by their cultural environments. Pinpointing the origin of this paradigmatic shift to a period of optimism about the ability of police to protect children, she explains how, by the middle of the twentieth century, police forces had intensified their presence in children's lives through juvenile curfew laws, police athletic leagues, traffic safety and anti-corruption campaigns, and school programs. The book describes the ways that seemingly altruistic efforts to integrate working-class youth into society evolved into pervasive supervision and surveillance, normalizing the police presence in children's lives. At the intersection of juvenile justice, policing, and childhood history, Youth Squad reveals how the overpolicing of young people today is rooted in well-meaning but misguided schemes of the mid-twentieth century.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.173 | 0.039 |
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".