Beliefs and attitudes about men's alcohol‐related sexual harassment and aggression (BAMASHA): Development and initial validation of a new scale
Bibliographic record
Abstract
BACKGROUND: Men's perpetration of sexual violence (SV) toward women in drinking venues is a pervasive yet understudied phenomenon with significant downstream consequences for women. Although men's negative attitudes and beliefs toward women play an important role in SV, current attitude measures are limited in that they do not focus on SV specific to drinking contexts, thereby precluding understandings of SV in this context. As such, we developed and evaluated a measure of beliefs and attitudes about men's alcohol-related sexual harassment and aggression (BAMASHA) toward women in drinking venues to better understand this ubiquitous problem. METHODS: = 22.66, SD = 2.09) completed an online survey that included 82 BAMASHA items developed to assess eight theoretical dimensions/sub-dimensions derived from past research. The survey also measured sexual aggression perpetration in drinking venues and well-established correlates of SV including drinking patterns, rape myth acceptance, hostility toward women, stereotypes about drinking women, and alcohol expectancies regarding sexual behavior. RESULTS: Item analysis resulted in a 24-item inventory with exploratory and confirmatory factor analyses suggesting a unidimensional factor structure. The resultant measure and its 12-item short form also explained sexual aggression perpetration toward women in drinking venues when controlling for associated constructs. CONCLUSIONS: Findings underscore the unique contributions of the BAMASHA for sexual aggression perpetration and its utility in the context of drinking venues compared to measures of attitudes and beliefs toward SV more generally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".