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Record W7005170913

Predicting and PreventingAggression and Violence Riskin High-Risk Girls:Lessons Learned and Cautionary Talesfrom the Gender and Aggression Project

2010· article· en· W7005170913 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCommitAggressionSuicide preventionPoison controlHuman factors and ergonomicsCriminal justiceInjury preventionIntervention (counseling)
DOInot available

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.255
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

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