Response to Editorial Comment: “Profiles of Cyberpornography Use and Sexual Well-Being in Adults”
Bibliographic record
Abstract
We thank the author of the editorial comment for the thoughtful remarks on our study. Research on the effects of pornography is still in its infancy. Despite strong social pressure for rapid closure, we should be cautious before concluding that pornography use is universally harmful or beneficial.1 Our contribution shows that subgroups of pornography users report differential sexual outcomes. Most of our sample was composed of recreational users reporting positive sexual outcomes, including higher sexual satisfaction. High-frequency compulsive use was restricted to 12% of our sample, and another 13% reported low use but significant distress and negative sexual outcomes that cannot be understood within an addiction model.2 An important step to determine treatment priorities for specific subgroups is to move toward empirical typologies of pornography users. To this end, one of the foremost priorities is to replicate our findings in large population-based samples. Further classification analyses also should consider preferences in the content of pornography viewed vs preferred sexual behaviors with a partner, underlying motivations, own- and partner pornography acceptance, and level of interest in sex. Other positive and negative outcomes of specific subgroups, such as intimacy difficulties, sexual self-esteem, sexual arousal or desire with a partner, and perceptions of masculinity and femininity, should be examined. Dyadic research also will help broaden our understanding of the interpersonal context of use.3 For example, the high level of distress observed in the non-compulsive group might be associated with solitary use or hidden from a partner. Further, longitudinal studies should determine how these subgroups evolve over time. In all cases, conducting high-quality scientific studies on this “new” phenomenon is essential.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".