Attitude Toward Sexual Aggression Against Women (ASAW) Scale: Evidence of Discriminant and Incremental Validity
Bibliographic record
Abstract
We tested the discriminant and incremental validity of scores on the Attitude toward Sexual Aggression against Women (ASAW) scale, a self-report measure that asks men to evaluate (very bad to not at all bad) a range of sexually aggressive behaviors against women. An online panel of 647 men completed the ASAW scale and self-report measures of other offense-supportive cognitions (rape myth acceptance, cognitive distortions, and beliefs regarding rape) and sexually aggressive behavior (past sexual aggression, likelihood of engaging in sexually aggressive behavior, and likelihood to rape). We hypothesized that (a) the ASAW would be distinct from other measures of offense-supportive cognition and (b) the ASAW would be independently associated with sexual aggression after accounting for the other measures. Supportive of discriminant validity, exploratory factor analyses revealed that ASAW items clustered together to form a distinct factor from other measures of offense-supportive cognition. Supportive of incremental validity, hierarchical multiple regression analyses indicated that the ASAW explained an additional 5% of the variance in past sexually aggressive behavior (ΔR² = .05, p < .006) and 4–6% of the variance in likelihood of engaging in sexual aggression (ΔR² = .04–.06, p < .006) after accounting for other measures of offense-supportive cognition. If future research finds further support for the construct validity of its scores, the ASAW should be used to study the potential causal role that attitudes may play in sexual aggression against women, and whether changing them can reduce the likelihood of engaging in this type of 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".