A Re-Examination of the Sexual Opinion Survey
Bibliographic record
Abstract
The Sexual Opinion Survey (SOS) has been one of the most widely used and influential measures in sexuality research cross-culturally since the 1980s. This scale is designed to measure individuals’ tendencies to avoid or approach sexual stimuli, also known as erotophobia-erotophilia. Although evidence of the scale’s validity has accumulated over the years, much of this work is limited to investigations of convergent and divergent validity. It has also been decades since the criterion validity of the original items has been reviewed and in that time some conceptual gaps in the scale’s item coverage have become apparent. Specifically, in this research we sought to determine if either the original or new SOS items of our own devising were better able to predict affective responses to a large range of sexual stimuli, compared to a variety of other sexuality measures (e.g., sociosexuality, sexual compulsivity, sexual health, etc.). To this end, we employed a machine learning method called Random Forests, in which all questionnaire items were entered as separate predictors. Results of this analysis indicated that a combination of the original and new SOS items better predicted affective responses to sexual stimuli than other sexuality measures that were tested. These findings confirm the validity of some of the existing SOS items but also suggest that further measurement refinement may be needed. Moreover, results of the exploratory factor analysis on the new SOS items identified a five-factor solution. Future validation efforts should confirm this structure.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".