Attitude Toward Sexual Aggression Against Women (ASAW) Scale: Development and Structural Validity
Bibliographic record
Abstract
We developed a new measure designed to facilitate research on the potential role of men’s attitude toward sexual aggression against women in gender-based sexual violence: the Attitude toward Sexual Aggression against Women (ASAW) scale. We created a large pool of items, in which participants were asked to evaluate how bad it would be if they engaged in various sexually aggressive behaviors in a variety of scenarios. Three independent samples of men recruited from online panels ( N = 380, 149, and 322) completed these items. Based on their responses, we retained 13 non-redundant items that had the most variance and covered a wide range of sexually aggressive behaviors (e.g., unwanted sexual touching; non-consensual sex), tactics (e.g., threatening to damage her reputation; using physical force), and contexts (e.g., the woman previously agreed to some sexual activity; the woman is intoxicated). An exploratory factor analysis found that all 13 ASAW items loaded strongly onto one factor, which suggests a unidimensional structure. If future research finds evidence for the construct validity of its scores, potential uses for the ASAW include risk assessment, treatment-related attitude-change, and research into the potential causal role of attitudes in sexual aggression against women.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".