Bibliographic record
Abstract
The present research examines sexual rejection dynamics in romantic relationships. In particular, this research focuses on 1) identifying the ways people reject their partners for sex, 2) understanding how distinct sexual rejection behaviours predict key relationship outcomes, and 3) assessing individuals’ accuracy in their perceptions of their partner’s sexual rejection behaviours. In Studies 1 and 2, a data-driven approach was used to identify a model of sexual rejection behaviours characterized by four distinct ways people reject a romantic partner’s sexual advances (i.e. reassuring, hostile, assertive, and deflecting). The unique effects of these behaviours for couples’ relationship satisfaction and sexual satisfaction were examined in cross-sectional (Study 3), experimental (Study 4), and daily experience (Study 5) study designs. Across studies, people who engaged in reassuring behaviours were more satisfied, as were their partners. In contrast, people who engaged in hostile behaviours were less satisfied, as were their partners. Mixed findings were found for outcomes associated with assertive and deflecting behaviours. Further, these patterns of findings were largely consistent across gender, relationship length, and sexual frequency. Robust evidence for the positive effects of reassuring rejection behaviours suggests these behaviours may serve an important relational function by helping couples sustain satisfaction in the context of sexual rejection, specifically because they convey responsiveness to a partner. Finally, specific patterns of perceptual accuracy for distinct sexual rejection behaviours were found. Individuals demonstrated low tracking accuracy for their partner’s reassuring and deflecting behaviours, and high tracking accuracy for their partner’s assertive and hostile behaviours. Further, individuals were found to overperceive their partner’s hostile and deflecting behaviours. The collective set of findings provides the first empirical investigation of the specific ways partners decline one another’s advances with the goal of understanding how couples can potentially navigate situations of conflicting sexual interests with greater success.
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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 | 0.016 |
| 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 | 0.002 |
| Research integrity | 0.000 | 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".