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Record W4407321359 · doi:10.1080/13552600.2025.2460108

Does drug and alcohol intoxication influence perceptions of risk and punishment in the context of campus sexual assault?

2025· article· en· W4407321359 on OpenAlexaff
Madison Wesenberg, Sandy Jung

Bibliographic record

VenueJournal of Sexual Aggression · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPunishment (psychology)Context (archaeology)Sexual assaultPsychologyPoison controlHuman factors and ergonomicsInjury preventionSuicide preventionAlcohol intoxicationOccupational safety and healthDrugMedical emergencyPerceptionRisk perceptionPsychiatryClinical psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Sexual violence is a prevalent issue in post-secondary institutions. Past research has shown that substance abuse is one of the many known risk factors for sexual violence, and yet intoxicated perpetrators are often labelled as less blameworthy or responsible for their actions. In this study, post-secondary employees and students were presented with a vignette of a campus sexual assault and asked to rate the perpetrator’s risk to reoffend and the appropriateness of sanctions varying in severity. Two severe sanctions, on-campus activity restriction and expulsion, were rated more appropriate for sober perpetrators than intoxicated ones. Students, specifically, rated less severe sanctions lower and more severe sanctions higher in appropriateness compared to employees. Perpetrator intoxication and sample type influenced sanction appropriateness, suggesting subjective factors influenced disciplinary decisions. Implications for improving response to campus sexual violence, such as using objective measures of risk and including students’ input when developing policies, are discussed.PRACTICE IMPACT STATEMENT The results of this study highlight the importance of using non-subjective measures of risk for campus sexual violence to ensure that risk is calculated in a way that is fair and supported by empirical research. These results also support the incorporation of robust sexual violence response policies to decrease subjectivity around disciplinary decisions, and therefore reduce the likelihood of discrepancies that could decrease perceptions of trust and confidence in post-secondary institutions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.340
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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