Predicting and PreventingAggression and Violence Riskin High-Risk Girls:Lessons Learned and Cautionary Talesfrom the Gender and Aggression Project
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».