Do Attitudes Toward Violence Affect Violent Behavior?
Bibliographic record
Abstract
Attitudes toward violence are important in theoretical explanations of violent behavior and efforts to reduce violent behavior. Though an association between attitudes and violent behavior has been demonstrated, most studies have used correlational/observational research designs. We conducted a randomized experiment to test the effect of attitudes toward violence on violent behavior with 285 men from the community. Participants were randomly assigned to receive material to make attitudes toward violence more negative or to a control condition. Violent behavior was then approximated by asking participants to select from a range of violent and nonviolent options in response to a series of interpersonal conflict vignettes. Participants in the negative attitude condition responded with less violence on the vignette questionnaire than did participants in the control condition (Cohen’s d = −0.23, 95% CI [−0.46, 0.01]). Participants also completed a measure of attitudes toward violence at the end of the experiment; more positive attitudes toward violence showed a strong association with more violent responding on the vignette questionnaire (r = .62, 95% bootstrapped CI [.54, .69]). Consistent with theory and practice, our findings suggest that attitudes toward violence may play a role in 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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".