Putting the Dyad into the Sexual Response Discussion: A Latent Class Analysis Using Ratings of Self and Partner
Bibliographic record
Abstract
In this study we explored the sexual response process in couple relationships. With a U.S. sample of 383 mixed-sex couples we found seven different classes of couple sexual response using Dyadic Latent Class Growth Analysis for ratings of self and partner about their most recent sexual experience. These classes ranged from synchronous High Arousal (31.6%) and Medium Arousal (27.7%) groups, to a few classes where one partner had a quick arousal process and the other partner had very low levels of arousal. Couples in these classes were differentiated on their levels of accuracy in understanding what their partner was experiencing, as one class had couples where men experienced higher arousal than women in the first part of the experience, but the male partner was aware of the discrepancy (Equifinality, 6.8%, i.e. couples start at different levels of arousal but end up at the same place), and another where men experienced higher arousal than women throughout the experience, but men inaccurately thought their partner also experienced higher arousal (Inaccurate Split, 7.3%). The seven classes had significantly different values on variables measuring the quality of the specific sexual experience. These classes also significantly differed on a variety of measures assessing the overall sexual relationship and the relationship as a whole. These findings counter the argument that the sexual response cycle is uniform for most couples.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".