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Record W3042942994 · doi:10.1177/0033294120939308

The Violent Behavior Vignette Questionnaire (VBVQ): A Measure of Violent Behavior for Research in Forensic and Non-Forensic Settings and Populations

2020· article· en· W3042942994 on OpenAlexafffund
Kevin L. Nunes, Chantal A. Hermann, Sacha Maimone, Maya Atlas, Brian A. Grant

Bibliographic record

VenuePsychological Reports · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVignettePsychologyForensic scienceClinical psychologyViolent crimePoison controlInjury preventionSocial psychologyCriminologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

The measurement of violent behavior presents serious challenges for research on violence. In the current article, we present initial tests of the construct validity of scores on the Violent Behavior Vignette Questionnaire (VBVQ), which consists of a series of interpersonal conflict vignettes with response options in a multiple-choice format designed to measure current violent behavior. Violent responses on the initial version of the VBVQ generally corresponded to independent indicators of physical aggressiveness and violent behavior among male university students, men in the community, and incarcerated male offenders. We then refined the VBVQ and again tested the validity of its scores in new samples of men in the community and incarcerated male offenders. In both samples, men who selected a violent response option on the VBVQ generally had much higher levels of physical aggressiveness and violent behavior than did men who selected non-violent response options. However, VBVQ responses were not associated with the number of violent offenses in offenders' official criminal records. Our findings provide some support for the use of the VBVQ in lab and correctional/forensic research, but further research is required to determine whether it offers advantages over other measures.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.152
GPT teacher head0.437
Teacher spread0.285 · 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.

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

Citations6
Published2020
Admission routes2
Has abstractyes

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