Female Juvenile Offenders: Differentiating Mechanisms of Antisocial Behavior by Neighborhood Disadvantage and Race
Bibliographic record
Abstract
The current study examined the impact of violence exposure and neighborhood disadvantage on antisocial behavior among Black (n = 69) and White (n = 53) female juvenile offenders.Using a multi-method research design, the study assessed neighborhood disadvantage through census level data, violence exposure through self report, and antisocial behavior through self report and official records.Self report of antisocial behavior was assessed at time of incarceration (Wave I) and post-release (Wave II).Results indicated that Black girls were significantly more likely than White girls to live in disadvantaged neighborhoods, but both reported similar levels of violence exposure.In terms of outcomes, no racial differences were observed with regard to self report of antisocial behavior but Black girls were significantly more likely to get rearrested for non-violent crimes.A divergent pattern of associations emerged; witnessing violence and peer abuse were indicative of Wave I antisocial behavior whereas age and time at risk were predictive of Wave II antisocial behavior.Neighborhood disadvantage was only associated with rearrest for non-violent crimes.Race specific pathways were explored using multiple group analyses.Parental physical abuse was associated with Wave II violent behaviors and recidivism for White girls whereas witnessing violence was associated with Wave II delinquent behaviors for Black girls.Results suggest that contextual characteristics play a role in offending among female juvenile offenders generally and Black female juvenile offenders, specifically.Race specific risk models warrant further investigation, and may help lawmakers and clinicians in addressing racial disparities in the justice system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".