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Record W2065371752 · doi:10.1177/0093854810365446

The Roles of Affect Dysregulation and Deficient Affect in Youth Violence

2010· article· en· W2065371752 on OpenAlexaff
Stephanie R. Penney, Marlene M. Moretti

Bibliographic record

VenueCriminal Justice and Behavior · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser UniversityCentre for Addiction and Mental Health
Fundersnot available
KeywordsAffect (linguistics)AggressionPsychopathyProsocial behaviorPsychologyJuvenile delinquencyPoison controlEmotional dysregulationDevelopmental psychologyHuman factors and ergonomicsInjury preventionClinical psychologyAntisocial personality disorderSuicide preventionPersonalitySocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Children with high dysregulated affect experience a range of emotional and behavioral problems, including aggression, delinquency, and low levels of prosocial behavior. Alongside this research, the psychopathy literature suggests that abnormally low levels of affect and emotional reactivity are associated with aggression and violence. The current study builds on prior research in the fields of affect regulation and psychopathy by testing the effects of affect dysregulation and deficient affect in predicting aggression and antisociality in 179 high-risk youth. Using structural equation modeling, results suggest that affect dysregulation and deficient affect are separate risk factors for aggression, as both constructs contributed independently to aggression while showing marginal relations with one another. Features of deficient affect, but not dysregulation, were robust predictors of violent and nonviolent offending. We discuss the importance of recognizing that diverse risk factors may lead to similar outcomes and highlight the heterogeneity in risk factors underlying aggressive behaviors.

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 score0.781
Threshold uncertainty score0.494

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 science0.0000.000
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 head0.035
GPT teacher head0.337
Teacher spread0.301 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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