An Exploration of Factors That Influence Enactment of Affirmative Consent Behaviors
Bibliographic record
Abstract
Affirmative sexual consent, which is ongoing, continuous, and clearly communicated, appears to be highly inconsistent with the way in which individuals actually negotiate their sexual interactions. The current qualitative research proposes an Information-Motivation-Behavioral Skills (IMB) model of sexual consent behavior and elicits, from young, sexually active informants, elements of information, motivation, and behavioral skills that appear to be necessary for engaging in affirmative consent behaviors. Eleven focus groups were conducted (N = 48 participants), which were semi-structured and guided by questions to tap into information, motivation, and behavioral skills aspects of ascertaining and expressing affirmative sexual consent. Two themes related to Information emerged from the data: (1) consent should be verbal, clear, and ongoing, and (2) consent should be natural and free-flowing. Two themes related to motivation were discussed: (1) affirmative consent is awkward, and (2) explicitly asking for consent can be good, but ascertaining consent indirectly is more comfortable. One theme related to behavioral skills was discussed: (1) social and personal factors influence how easy or hard it is to explicitly discuss consent to sexual activity. Understanding factors that influence affirmative sexual consent is an essential step toward developing interventions to promote consensual sexual interactions and explicit sexual communication.
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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 | 0.039 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".