“Let’s not talk about it”: examining the interpersonal context of pornography use by investigating patterns of communicative avoidance
Bibliographic record
Abstract
We investigated the extent to which individuals deliberately avoid the topic of pornography use with their partner and how such avoidance influences the association between pornography use and sexual satisfaction. We reasoned that such communicative avoidance may account for some of the negative relational effects associated with pornography use. A sample of 191 participants in mixed-sex relationships completed online measures of pornography use, sexual satisfaction, and domain-specific and overall measures of communicative avoidance. Men tended to avoid the topic of pornography use more than other topics that have the potential to cause relationship conflict. Women did not avoid the topic of pornography use more or less than other relationship topics. Controlling for pornography use, men and women who were more likely to avoid the topic of pornography use were less likely to be sexually satisfied; this association remained significant for men (but not women) after controlling their overall communicative avoidance. Lastly, men who were high pornography users and reported high levels of overall communication avoidance were most likely to report lower sexual satisfaction. Our findings underscore the need to pay careful attention to relational processes and dynamics when investigating the influence of pornography use on intimate relationships.
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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 | 0.002 | 0.012 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".