Using reaction time procedures to assess implicit attitudes toward violence in a nonconvicted male sample
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we sought to capture implicit attitudes toward violence by administering response latency measures. We then examined their associations with explicit (e.g., assessed with self-report) attitudes toward violence and self-reported violent behavior in a combined sample of males from a Canadian university and males from the general community (N = 251; 156 students and 95 community members). To date, there have been mixed findings regarding these associations; some of this inconsistency may be due to the difficulty in accurately conceptualizing and assessing implicit attitudes toward violence. Therefore, we administered three response latency measures to assess this construct: a violence evaluation implicit association test (VE-IAT), a personalized VE-IAT (P-VE-IAT), and a violence evaluation relational responding task, along with three self-report measures of explicit attitudes toward violence and three self-report measures of violent behavior. More positive implicit attitudes toward violence were related to more positive explicit attitudes toward violence (for VE-IAT and P-VE-IAT; r = 0.18 to 0.22), greater likelihood of violence (for VE-IAT; r = 0.18 and for P-VE-IAT; r = 0.16), and greater propensity for violence (for the VE-IAT; r = 0.16). All measures of explicit attitudes toward violence and violent behavior were moderately to strongly associated with one another (r = 0.42 to 0.81). Furthermore, implicit attitudes toward violence explained additional variance in some violent outcomes above explicit attitudes alone. Our findings suggest that scores on certain reaction time measures are important for understanding likelihood and propensity for violence, especially when combined with explicit attitude measures.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it