Untangling the Porn Web: Creating an Organizing Framework for Pornography Research Among Couples
Bibliographic record
Abstract
Research exploring the correlates, moderators, and potential consequences of viewing pornography for romantic couples has surged in recent years. Research in this area has primarily focused on the question of whether viewing pornography for either partner (or together) is related to enhanced, diminished, or has no effect on relational well-being. However, this narrow scholarly focus and the continued methodological limitations of research in this area have made synthesizing or drawing broad conclusions about pornography use from this scholarship difficult. One specific limitation of this area is the lack of any broad organizational framework that could help scholars categorize existing research while also laying the groundwork for future scholarship. In this paper, we argue for such a framework and suggest that relational pornography scholarship could be organized across five broad dimensions: the nuances of the content viewed, individual background factors, personal views and attitudes, a couple's relational context, and couple processes. We provide a justification for these five areas and then discuss how this framework could help organize and structure the research in this area moving forward.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".