398 Effects of Pornography Use on the Couple Relationship: Results of “Bottom-Up,” Participant-Informed, Qualitative and Quantitative Research
Bibliographic record
Abstract
Considerable research has emerged concerning effects of pornography on couple relationships. “Top-down” close-ended, quantitative investigations have targeted a limited number of primarily harm-focused effects for study. A small body of qualitative research offers tantalizing glimpses of potential effects of pornography that have been overlooked in the “top-down” close-ended, harm-focused research tradition. The aim of the current two “bottom-up” participant-informed, qualitative and quantitative studies was to identify a range of participant reported effects of pornography on the couple relationship that may have been overlooked in “top-down,” close-ended, harm focused quantitative studies. Study 1: An online survey recruited men (n = 219) and women (n = 211) who were currently in a heterosexual relationship where at least one partner had experience with pornography use. Open-ended questions probed participants’ perceptions of the impacts that their solitary pornography use, their partners’ solitary pornography use, and/or their shared pornography use, has had on their relationships. Responses were analyzed using Thematic Analysis (Braun & Clarke, 2006). Study 2 recruited both members of 200 couples, diverse in age, religion, political orientation, and other characteristics, and employed qualitative and quantitative assessments of effects of pornography on their couple relationship.
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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 | 0.063 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".