Talk about It, Don’t Type about It: How In-Person and Technology-Mediated Sexual Self-Disclosure Relate to Sexual Satisfaction
Bibliographic record
Abstract
Sexual self-disclosure (SSD) is when a person shares information about their sexuality with another person. Technology-mediated communication is pervasive in modern society, yet researchers have not distinguished between SSDs that occur in-person versus in technology-mediated contexts. Using the Interpersonal Exchange Model of Sexual Satisfaction, researchers previously found that SSD predicts sexual rewards, costs, and satisfaction. In this study, we (1) compared cisgender/transgender men’s and women’s frequency (how much) and breadth (how many topics) of SSD via typed technology and in-person (H1, H2), and (2) examined the extent to which the frequency and breadth of SSD in each context predicted perceived sexual rewards, comparison of sexual rewards, and in turn sexual satisfaction while controlling for relationship satisfaction (H3, H4, H5, H6). Undergraduate students (N = 450) completed an online survey that assessed SSD in each context, perceived sexual rewards and costs, comparison of own and partner’s sexual rewards and costs, and sexual and relationship satisfaction. Participants reported more frequent and greater breadth of SSD in-person than via technology. We also found that women disclosed more sexual topics than men in-person but not through typed technology. Using path analyses, a greater frequency of SSD in-person predicted greater perceived sexual rewards and comparison sexual rewards, and in turn, greater sexual satisfaction. The frequency of SSD via typed technology and the SSD breadth in either context did not predict exchanges or sexual satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".