“Yes doesn’t always mean yes, but no means no”: Exploring the perceived ambiguities in university students’ experiences of sexual consent
Bibliographic record
Abstract
This article aims to explore situations of consent and non-consent in university students to understand regulatory mechanisms that contribute to the perceived ambiguity of students’ experiences of sexual consent. To apprehend these experiences, 37 semi-directed interviews were conducted and analyzed using thematic analysis, systemic analysis, and contextualizing analysis. The results illustrate the existence of a dichotomy between students’ knowledge of consent and the practice of consent. This ambiguity appears as an integral part of the act of consent. It was apparent in the students’ understanding, affirming, retracting, and decoding of consent. The authors’ analysis highlights the regulating mechanisms, that is, internal and external injunctions at play in the act of consent. Three injunctive mechanisms were identified: relational injunctions to consent, social injunctions to consent, and men’s unrestricted access to women’s bodies. These injunctive mechanisms act in different manners according to one’s social position, individual characteristics, the type of relationship and the social spaces. By facilitating or hindering consent, they make it a profoundly ambiguous act.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| 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".