Implicit and Explicit Evaluations of Sexual Aggression Predict Subsequent Sexually Aggressive Behavior in a Sample of Community Men
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current longitudinal study explored the extent to which implicit and explicit evaluations of sexual aggression predict subsequent sexually aggressive behavior. Participants (248 community men recruited online) completed measures of implicit and explicit evaluations and self-reported sexually aggressive behavior at two time points, approximately 4 months apart. Implicit and explicit evaluations of sexual aggression at Wave 1 had small significant and independent predictive relationships with sexually aggressive behavior at Wave 2, while controlling for sexually aggressive behavior at Wave 1. This is the first study to test whether implicit and explicit evaluations predict subsequent sexually aggressive behavior. Our findings are consistent with the possibility that both implicit and explicit evaluations may be relevant for understanding and preventing subsequent sexually aggressive behavior. If these findings can be replicated, evaluations of sexual aggression should be studied with more rigorous methodology (e.g., experimental design) and correctional/forensic populations, and possibly addressed in risk assessment and interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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