Dominant Heterosexual Sexual Scripts in Emerging Adulthood: Conceptualization and Measurement
Bibliographic record
Abstract
Sexual script research (Simon & Gagnon 1969, 1986) bourgeoned following Simon and Gagnon’s groundbreaking work. Empirical measurement of sexual script adherence has been limited, however, as no measures exist that have undergone rigorous development and validation. We conducted three studies to examine current dominant sexual scripts of heterosexual adults, and to develop a measure of endorsement of these scripts. In Study 1, we conducted three focus groups of men (n=19) and four of women (n=20) to discuss the current scripts governing sexual behavior. Results supported scripts for sex drive, physical and emotional sex, sexual performance, initiation and gatekeeping, and evaluation of sexual others. In Study 2, we used these qualitative findings to develop a measure of script endorsement, the Sexual Scripts Scale. Factor analysis of data from 721 participants revealed 6 interrelated factors demonstrating initial construct validity. In Study 3, confirmatory factor analysis of a separate sample of 289 participants supported the model from Study 2, and evidence of factorial invariance and test-retest reliability was obtained. This paper presents the results of these studies, documenting the process of scale development from formative research through to confirmatory testing, and suggests future directions for the continued development of Sexual Scripting Theory.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".