An exploration of prevalence, variety, and frequency data to quantify online sexual activity experience
Bibliographic record
Abstract
People use the Internet for a wide range of online sexual activities (OSA): behaviours that involve sexual content, topics, and stimuli. Yet, current OSA summary statistics provide little perspective on patterns of OSA experience because researchers have not compared multiple indicators of experience within the same sample. We explored the prevalence, variety, and frequency of young men and women's experience with three OSA subtypes: non-arousal (e.g., accessing sexual health information), solitary-arousal (e.g., viewing pornography), and partnered-arousal (e.g., sending sexually explicit messages). We examined patterns in experience with specific OSAs, subtypes of OSAs, OSAs overall, and differences related to gender across the lifetime and recently. Young adults (N=239) at a Canadian University completed a survey that included a new measure of 48 specific OSAs, representing the three subtypes. All participants reported at least one OSA experience in their lifetime (ranging 1–38). Although the prevalence and variety of experience was greater across the lifetime than recently, this difference was small and the pattern of results remained the same. Frequency of experience appeared greater for specific OSAs compared to OSA subtypes or overall. Frequency of specific OSAs were greater for the subgroup of participants who had engaged in the activity recently compared to the full sample. Significantly more men than women reported solitary-arousal OSA, and men reported greater variety and frequency of this subtype. This gender difference in prevalence and frequency only held for viewing sexually explicit pictures and videos online. We discuss implications for sexual scripts, researchers, clinicians, and educators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".