How Does Alcohol Contribute to Sexual Assault? Explanations from Laboratory and Survey Data
Bibliographic record
Abstract
This article summarizes the proceedings of a symposium of the 2001 RSA Meeting in Montreal, Canada. The chair was Antonia Abbey and the organizers were Tina Zawacki and Philip O. Buck. There were four presentations and a discussant. The first presentation was made by Maria Testa whose interviews with sexual assault victims suggest that there may be differences in the characteristics of sexual assaults in which both the victim and perpetrator were using substances as compared to when only the perpetrator was using substances. The second presentation was made by Tina Zawacki whose research found that perpetrators of sexual assaults that involved alcohol were in most ways similar to perpetrators of sexual assaults that did not involve alcohol, although they differed on impulsivity and several alcohol measures. The third presentation was made by Kathleen Parks who described how alcohol consumption affected women's responses to a male confederate's behavior in a simulated bar setting. The fourth presentation was made by Jeanette Norris who found that alcohol and expectancies affected men's self-reported likelihood of acting like a hypothetical sexually aggressive man. Susan E. Martin discussed the implications of these studies and made suggestions for future research.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".