Moralizing Consent: Three Field Studies Testing a Student-Led Intervention at University Parties
Bibliographic record
Abstract
Moralization is the process by which preferences become moral values. We investigated a practice that is changing its moral status on college campuses in the United States: affirmative consent to sexual activity. We tested whether messages given to students just before they entered a party impacted their thinking about consent in moral terms-i.e., as a clear issue, with broad consensus, and an imperative to action. At two social clubs on a college campus in 2017, we randomly assigned moralistic vs. informational messages about consent, delivered at the party's door. At the club that had pre-existing messaging about consent, the moralistic (vs. informational) message increased students' thinking about consent in moral terms. By contrast, in the club without prior consent messaging, the informational (vs. moralistic) pledge increased students' thinking about consent in moral terms. We then investigated and found weak evidence for a small reduction in administrative-level student conduct complaints compared to prior and subsequent years as a result of a one-night consent message treatment unique to each of the 12 clubs hosting a party. Theoretically, our findings make progress toward understanding processes of moralization. Pragmatically, they suggest the importance of locally tailored messages that reflect and shape the values of social groups.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".