Bullying behaviors in female and male adolescent offenders: prevalence, types, and association with psychosocial adjustment
Bibliographic record
Abstract
Abstract Despite the surge of research on bullying, few studies have examined bullying in young offenders, particularly female young offenders. This study investigated the prevalence, types, and correlates of bullying behaviors in 193 male and 50 female incarcerated adolescents from nine young offender facilities. Overall, 37% of participants identified themselves as bully‐victims, 32% as pure bullies, 23% as not involved, and 8% as pure victims. In comparison to males, females were more likely to report being involved with bullying in some capacity, particularly as pure victims, and being bullied by sexual touching and comments. Pure victims reported higher rates of psychological distress and suicidal behaviors than those youth not involved in bullying, and pure bullies were more likely to have been previously incarcerated and affiliated with a gang. Bully‐victims reported the highest rates of previous abuse, peer victimization in the community, drug use, and suicide attempts while in custody. All groups, including pure victims, reported high rates of bullying others in the community. Treatment providers should recognize that offenders who are victims are often bullies as well, and be alert to broad mental health needs among victims and bully‐victims. Given the prevalence and potential serious consequences of bullying, the development of anti‐bullying policies appears to be an important step in recognizing and reducing bullying. Aggr. Behav. 00:1–16, 2005. © 2005 Wiley‐Liss, Inc.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".