Evaluative Attitudes of Sexual Aggression Towards Women from Men Who Commit Sexually Aggressive Acts Towards Women
Bibliographic record
Abstract
It is well-documented in theory and in research that evaluative attitudes predict subsequent behaviour, however the association of evaluative attitudes and sexually aggressive behaviour have been scarcely researched, and where they are researched, they lack consistent definitions/measures.The given research investigated the association of evaluative attitudes of sexual aggression towards women and the perpetration of sexual aggression towards women using a newer version of an evaluative attitudes of sexual aggression towards women measure (the EASAW).Data were collected from 259 men at the Missouri Sex Offender Program at the Farmington Correctional Center in Jefferson City, Missouri.EASAW mean scores of those with a history of sexual violence towards women and those without a history of sexual violence towards women were compared, with no significant differences and small effect sizes found, implying lack of an association between evaluative attitudes of sexual aggression towards women and the perpetration of sexual aggression towards women.Future research should continue to investigate this relationship that address shortcomings of this study, such as: more diverse samples and experimental manipulation.If the findings of this study can be duplicated, measures of evaluative attitudes of sexual aggression towards women should not be added to risk assessment batteries or addressed in treatment programs of sexual offending towards women, and instead should focus on constructs that are better-demonstrated in research. Keywords: evaluative attitudes,
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".