Using reaction time procedures to assess implicit attitudes toward violence in a nonconvicted male sample
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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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".