MétaCan
Menu
Back to cohort
Record W2168602578 · doi:10.1177/0093854813519629

A Meta-Analysis Exploring the Relationship Between Psychopathy and Instrumental Versus Reactive Violence

2014· article· en· W2168602578 on OpenAlexaff
Julie Blais, Elizabeth Solodukhin, Adelle E. Forth

Bibliographic record

VenueCriminal Justice and Behavior · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOntario Tech UniversityCarleton University
Fundersnot available
KeywordsPsychopathyPsychologyFacet (psychology)Poison controlInterpersonal violenceDeviance (statistics)Clinical psychologyHuman factors and ergonomicsInjury preventionSuicide preventionDevelopmental psychologyPersonalityBig Five personality traitsMedicineSocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

The present meta-analysis explored the relationship between psychopathy and instrumental and reactive violence with a focus on factor and facet scores. A total of 53 studies (reporting on 55 unique samples, N = 8,753) from both published and unpublished sources were included. Results from random-effects analyses indicated moderate and significant relationships between psychopathy and both instrumental and reactive violence. There was some evidence that the Interpersonal facet was more important for instrumental violence, while Factor 2 (social deviance) was more important for reactive violence. The Lifestyle facet appeared important in explaining both violent outcomes. Effect sizes were significantly smaller for clinical rating scales compared with informant and self-report scales. Significant between-study variability was partly explained by mean age of the sample and type of outcome measure. The current findings do not support the conclusion that psychopathy is more related to instrumental violence as opposed to reactive violence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.036
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0120.037
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.363
GPT teacher head0.399
Teacher spread0.036 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations258
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCriminal Justice and BehaviorSame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207