Predicting and PreventingAggression and Violence Riskin High-Risk Girls:Lessons Learned and Cautionary Talesfrom the Gender and Aggression Project
Bibliographic record
Abstract
Youth violence is a serious public health concern when viewed in light of the costs incurred by the medical, social service, and criminal justice systems. Since the late 1980s, there has been a steady increase in violent crimes committed by youth in both Canada and the U.S. Although more recent rates of youth violence are decreasing, they have remained significantly above the averages recorded in the early to mid-1980s. Rates of official violent offending among adolescent girls in particular have been increasing at faster rates compared to boys, and self-report data shows that the gap between girls and boys’ rate of engagement in violence is closing. In light of these trends, assessing and reducing violence risk among youth are high-priority objectives. Increasing knowledge surrounding the precursors of youth violence represents an essential step in this regard, as well as in the development of research-based prevention and intervention approaches. Several large-scale, longitudinal research studies have responded to this need, identifying numerous risk factors at the individual, family, school, peer, and community levels that predict future violence and criminality. Accurately assessing and identifying those youth who are likely to commit future violence also has implications for many decisions made within the juvenile justice system (e.g., decisions regarding waiver to adult court, sentencing, and release). Significant advances in adult violence risk assessment have paved the way for the development of similar tools with adolescents. However, the vast majority of existing risk assessment schemes for use with adolescents do not factor in gender relevant information; that is, the assumption in most measures is that the factors contributing to violence operate in a similar manner across males and females. As members of our research team have noted, however, this assumption has not been empirically tested via prospective studies including sufficient numbers of female participants. Given that most risk assessment measures include variables based on their predictive ability in all-male samples, it is possible that qualitatively different risk factors are required to predict violence among females, or that similar risk factors exist, which carry differential significance in male and female samples. The next section of this review outlines some of the key challenges involved in assessing violence risk in girls, and the caveats of extending our current knowledge base—based largely on males—to young females.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".