<i>That’s the Way</i> ( <i>I like It</i> ): Latent Profiles of Sexual Power Dynamics and Gender Roles
Bibliographic record
Abstract
Sexual power dynamics (SPD) regroup sexual interests of dominance, submission, and tender sex. Interest toward sexual domination and/or submission has historically been pathologized and linked with higher levels of sexo-relational distress. Guided by sexual script theory, the consideration of one’s attitudes toward gender roles (GR), as either gender transcendent or gender-linked, might help identify different profiles of clients in sex therapy. To our knowledge, studies have yet to explore the different profiles while considering both SPD and GR. The scarcity of research on these variables limits our understanding of their implications for individuals’ sexo-relational well-being. The current study aimed to explore the presence of latent profiles based on SPD and GR reported by clients in sex therapy, and to compare the resulting profiles on possible correlates (i.e., sociodemographic characteristics, romantic attachment, relationship and sexual satisfaction). A sample of 325 adults in sex therapy, completed self-reported questionnaires at intake. Results revealed three profiles: (1) Dominant stereotypical thinkers (3.7%), (2) Tender conflicted traditionalists (15.4%), and (3) Flexible and coherent lovers (80.9%). Inter-profile comparisons revealed significant differences regarding gender, age, avoidant attachment and relationship satisfaction. Overall, results suggest the presence of an intricacy between SPD and GR, which would benefit from further exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".