A Qualitative Exploration of Undergraduate Student Perspectives \nof Sexual Consent Within a Sexual Script Framework
Bibliographic record
Abstract
There appear to be widespread misunderstandings and disagreement regarding the definition and execution of sexual consent among postsecondary students. Without a clear understanding of what constitutes sexual consent, navigating consent-related situations can be challenging for individuals. Sexual script theory may explain gaps in consent knowledge by highlighting the social normative references, or sexual scripts, individuals may rely on when knowledge of consent is insufficient or challenging to apply. The goal of the current study was to qualitatively explore Canadian undergraduate students' perceptions and experiences of sexual consent within the framework of sexual script theory. Using focus groups, N = 56 undergraduates discussed perceptions of sexual consent, under what circumstances they perceive it to be required, potential "grey areas" of sexual consent and how they are navigated, and how gender may intersect with sexual consent. Transcripts were analyzed within a framework of sexual script theory using inductive coding and thematic analysis. Although participants understood Canadian legally codified scripts well, they seemed to rely on socio-cultural and gendered sexual consent scripts when legal scripts were insufficient or challenging to apply, particularly in ambiguous consent scenarios. The findings suggest that beyond providing educational opportunities and interventions within formal school settings that encompass a wider range of sexual consent scenarios, there is a need to address socio-cultural norms/sexual scripts regarding consent within the broader population.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".