What is the “Sex” in Sex Addiction? Problem Behaviors Reported Among Those Endorsing Compulsive Sexual Behavior
Bibliographic record
Abstract
Recent years have seen a surge in research related to compulsive, excessive, or out-of-control sexual behaviors. Yet, little is known about which behaviors people might experience as compulsive. Using YouGov America, a non-probability sample was collected, matched to U.S. representative norms, stratified, and weighted based on sample characteristics to ensure representativeness (N = 2,806; Mean Age = 48.9, SD = 17.3). Participants reported if they had experienced any concerns about their sexual behaviors being either “out of control” or “an addiction.” Participants who endorsed such concerns were then asked to indicate which behaviors had led to such concerns, using a checklist of 11 potentially overlapping sexual behaviors (e.g. frequent casual sexual encounters, using apps to find sexual partners). Men—both heterosexual and gay/bi/other–were more likely to report concerns that their sexual behaviors were an addiction, relative to heterosexual women. Relative to heterosexual women, men of any sexual orientation were more likely to report pornography use as a specific behavior of concern, and less likely to report partnered sexual behaviors. More religious participants were more likely to endorse masturbation as a behavior of concern and less likely to endorse frequent casual sexual encounters.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".