“I’ve Learned to Convert My Sensations into Sounds”: Understanding During-Sex Sexual Communication
Bibliographic record
Abstract
Research generally supports the idea that sexual communication is beneficial to partners in committed relationships. However, much existing sexual communication research has a verbal communication bias and has examined sexual communication in non-sexual contexts, neglecting a wide variability of other forms of communication that occur during sex. Thus, from a sexual scripts theoretical framework, the purpose of the present study was to: (1) explore how individuals communicate needs, desires, pleasure, and displeasure to their partners during sex; (2) investigate perceptions of during-sex sexual communication's purposes; and (3) document individuals' perceived barriers and facilitators to during-sex sexual communication. Data from 27 interviews conducted among individuals in committed different-gender relationships (15 women, 11 men, 1 queer person; 21-68 years old), were analyzed using thematic analysis. Participants reported communicating using a combination of verbal, vocal, and bodily forms of communication. Most participants indicated that communicating during sex increased sexual pleasure and emotional intimacy and was useful for clarifying doubts and reducing insecurities. Many individuals nonetheless reported avoiding verbal communication during sex to preserve the mood, protect a partner's feelings, and avoid experiencing negative emotions and a partner's judgment. Sexual communication was also described as a skill that is developed over time and through the development of sexual subjectivity. Implications for sexual script theory and future sexual communication research are discussed.
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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 | 0.007 |
| 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 | 0.012 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".