Exposure to violence in incarcerated youth from the city of São Paulo
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the extent of exposure to community violence among delinquent Brazilian youth in the 12-month period prior to their incarceration and to identify factors associated with this exposure. METHOD: With an oversampling of girls, a cross-section of youth under 18 years of age from juvenile detention units in the city of São Paulo, Brazil completed a structured interview. Key items related to exposure to violence (witnessed and experienced) were drawn from the Social and Health Assessment questionnaire to cover the 12-month period prior to incarceration. RESULTS: Participants (n = 325, 89% boys) reported high rates of exposure to violence with largely similar levels for boys and girls. Being threatened with physical harm, being beaten or mugged and/or shot at were the most common forms of violence experienced. After controlling for demographic and family variables, the fact of having peers involved in risk behavior, easy access to guns and previous involvement with the justice system were associated with witnessed violence; whereas having slept on the street was the only variable associated with experienced violence. CONCLUSION: This group of youth was exposed to high levels of violence and other adverse experiences. Future research should examine the effectiveness of strategies aimed at reducing the exposure to violence of high-risk youth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".