From School Halls to Shopping Malls: Multilevel Predictors of Police Contact In and Out of School
Bibliographic record
Abstract
Objectives: Individual- and school-level factors associated with youth being stopped, searched, or arrested in school are identified. Correlates of community-based contact are also examined. Methods: Longitudinal student surveys and corresponding school-level data come from 21 middle and high schools in 6 districts in St. Louis County, Missouri. Multilevel multinomial logistic regression was used to assess factors related to a three-category dependent variable, distinguishing youth with: (1) no police contact, (2) in-school contact, and (3) out-of-school contact. Independent variables capture student-level demographics, behavior, experiences, and perceptions and school-level characteristics and practices. Results: Factors associated with in-school contact include substance use, peer associations, prior contact, and prior school sanctions. Odds of school-based contact also increase when youth are less aware of school rules and perceive greater disorder. Among school-level characteristics, only officers responding to school problems is significantly associated with in-school contact. Conclusions: There is some consistency in individual-level factors associated with police contact across locations, particularly related to prior sanctions, but findings highlight potential mechanisms that vary across contexts. This study also provides evidence that some schoolwide responses may contribute to youth's likelihood of having police contact in school, but solutions should consider the fluidity of contact in schools and communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".