An Assessment of the Cues College Students Interpret From a Sexual Partner to Determine They Are Refusing
Bibliographic record
Abstract
The purpose of this study was to assess the cues college students use to determine a sexual partner is refusing vaginal-penile sex (i.e., refusal interpretations). As a secondary aim, we explored the influence of item wording ( not willing/non-consent vs refusal) on college students’ self-reported refusal interpretations. A sample of 175 college students from Canada and the United States completed an open-ended online survey where they were randomly assigned to one of two wording conditions ( not willing/non-consent vs refusal); students were then prompted to write about the cues they used to interpret their partner was refusing. An inductive coding procedure was used to analyze open-ended data. Themes included explicit and implicit verbal and nonverbal cues. The refusal condition elicited more explicit and implicit nonverbal cues than the not willing/non-consent condition. Frequency results suggested men reported interpreting more explicit and implicit verbal cues. Women reported interpreting more implicit nonverbal cues from their partner. Our findings reflect prior research and appear in line with traditional gender and sexual scripts. We recommend researchers consider using the word refusal when assessing the cues students interpret from their sexual partners as this wording choice may reflect college students’ sexual experiences more accurately.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".