The Violent Behavior Vignette Questionnaire (VBVQ): A Measure of Violent Behavior for Research in Forensic and Non-Forensic Settings and Populations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".