024 An Information–Motivation–Behavioral Skills Model of Sexual Consent
Bibliographic record
Abstract
Sexual consent is emerging as an important, controversial, and poorly articulated construct that is nonetheless crucial to ensuring the appropriate conduct of sexual interactions. Institutional administrators and legislative authorities face the obligation to appropriately and effectively address the problem of nonconsensual sexual interactions. Despite the significance of sexual consent, however, a systematic conceptualization of factors that contribute to sexual consent-related behaviors remains to be better developed. The current research articulates an Information—Motivation—Behavioral Skills (IMB) model of sexual consent that aims to provide a comprehensive understanding of factors that contribute to the enactment of sexual consent-related behavior. The IMB conceptualization was selected as a theoretical model of sexual consent due to its comprehensive capture of factors that conceptually and empirically are related to enactment of sexual consent behaviors and due to its empirical support in related areas of sexual behavior. The model asserts that accurate and actionable sexual consent information, personal and social motivation to act on it, and behavioral skills for acting on it effectively, are key determinants of the performance of sexual consent behaviors that result in clear and unambiguous understanding of the willingness or unwillingness of individuals to participate in a sexual interaction.
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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 | 0.002 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".