Evaluation and Refinement of the Violent Behavior Vignette Questionnaire (VBVQ)
Bibliographic record
Abstract
Objective: The current research sought to explore the dimensionality, optimization, and construct validity of the Violent Behavior Vignette Questionnaire (VBVQ; Nunes et al., 2021). Method: Secondary data analyses were conducted on three samples (N = 1,339) with data on the VBVQ, the Violent Behaviour Scale (VBS; Nunes et al., 2015), and the Physical Aggression Scale of the Aggression Questionnaire (PA-AQ; Buss & Perry, 1992). Results: The CFA results indicated a one-factor model. Findings from the hierarchical regressions support the current configuration of the VBVQ. Correlations between some alternative versions of the VBVQ and PA-AQ/VBS total scores were comparable to those of the current version, suggesting that certain vignettes may be suitable for removal. The results demonstrate the VBVQ’s ability to differentiate by the known group of younger/older, but not single/married. Conclusion: The current research supports the VBVQ’s unidimensional structure, current scoring, and ability to differentiate by age.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.000 |
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.
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".