Bottoms Up: Interpretations of Consent and Culpability with Alcohol Use in Sexual Assault Scenarios
Bibliographic record
Abstract
In legal and public domains, intoxication of sexual assault perpetrators and victims has been shown to impact the interpretation of culpability for one’s actions. In general, research has demonstrated that perpetrators of violent crimes, such as sexual assault, who have consumed alcohol is perceived as less responsible for their crime, while intoxicated victims of those crimes are perceived as more responsible for the incident. The present study was designed to further investigate the relationship between alcohol consumption, the sexual history of the persons involved, and sexual assault. Undergraduate participants were presented with a vignette depicting an ambiguous sexual assault scenario, a judgment questionnaire, and self-report measures. The vignettes differed according to alleged perpetrator intoxication (sober/mild/moderate/extreme), alleged victim intoxication (sober/mild/moderate/extreme), and the sexual history of the couple (no sexual history/sexual history). We assessed participants’ perceptions of perpetrator/victim consent and culpability, as well as their overall assessment of the scenario (i.e., how violent/severe) and general perceptions of alcohol use and sexual assault allegations. Overall, victims were perceived as more responsible as their intoxication increased, while extremely intoxicated perpetrators were viewed as less responsible for the assault. Unexpectedly, previous sexual history had no effect. Implications of these findings will be discussed. Department: Psychology Faculty Mentor: Dr. Kristine Peace
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".