Racial differences in the associations of neighborhood disadvantage, exposure to violence, and criminal recidivism among female juvenile offenders
Bibliographic record
Abstract
The current study examined the impact of exposure to violence and neighborhood disadvantage on criminal recidivism among Black (n = 69) and White (n = 53) female juvenile offenders. Participants were girls between the ages of 13 and 19 (M = 16.8; SD = 1.2) who were sentenced to secure custody. Using a multi-method research design, the study assessed neighborhood disadvantage through census level data, exposure to violence through self-report, and criminal recidivism through official records. Results indicated that Black girls were significantly more likely than White girls to live in disadvantaged neighborhoods, but both reported similar levels of parental physical abuse and witnessing neighborhood violence. In structural equation models, neighborhood disadvantage and witnessing neighborhood violence were indicative of future recidivism for the group as a whole. However, multiple group analyses indicated the existence of race specific pathways to recidivism. Witnessing neighborhood violence was associated with recidivism for Black girls while parental physical abuse was associated with recidivism for White girls. Results suggest that characteristics within the neighborhood play a considerable role in recidivism 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".