Thinking Clearly About Violent Cognitions
Bibliographic record
Abstract
The purpose of the current study was to explore whether measures such as the Violence Scale of the Revised Measures of Criminal Attitudes and Associates (MCAA-R-V) and the Criminal Attitudes to Violence Scale (CAVS) assess attitudes toward violence (i.e., evaluation of violence) and whether attitudes and the cognitions assessed by the MCAA-R-V and CAVS are independently associated with violent behavior. Participants (568 undergraduate students) completed the MCAA-R-V and the CAVS, as well as measures of evaluation of violence, evaluation of violent people, identification of self as violent, and past violent behavior. Exploratory factor analyses revealed that the MCAA-R-V and CAVS items formed correlated but distinct factors from the items of the evaluation of violence, evaluation of violent people, and identification of self as violent scales. Regression analyses indicated that evaluation of violence and identification of self as violent correlated with violent behavior independently of the MCAA-R-V and CAVS. Our results suggest that attitudes toward violence may be distinct from other cognitions often referred to as "attitudes" in the criminological literature, and both attitudes and these other cognitions may be relevant for understanding violent behavior.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".