Gender differences in risk factors for violence: an examination of the predictive validity of the Structured Assessment of Violence Risk in Youth
Bibliographic record
Abstract
The research literature on predicting violence is particularly lacking in specifying risk factors for violence in adolescent girls. The recently developed Structured Assessment of Violence Risk in Youth [SAVRY; Borum et al., 2006] shows promise as it is empirically derived and incorporates dynamic factors in its assessment of risk. To date, there exists little information attesting to the reliability and validity of the SAVRY, and few investigations of the SAVRY's utility across gender. This study investigated the SAVRY in a sample of 144 high-risk adolescents (80 males and 64 females), focusing on gender discrepancies in the predictive utility of the measure. Results indicate that the SAVRY moderately predicts violent and non-violent reoffending in the entire sample, and also suggest that the SAVRY operates comparably across gender. Although not precluding the existence of gender-specific domains of risk, current results suggest that validated risk factors in boys hold relevance for the prediction of violence and delinquency in girls.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".