Why Do People Watch Pornography? Cross-Cultural Validation of the Pornography Use Motivations Scale (PUMS) and Its Short Form (PUMS-8)
Bibliographic record
Abstract
Motivations for pornography use may vary across gender identities, sexual orientations, and geographical regions, warranting examination to promote individual and public health. The aims of this study were to validate the Pornography Use Motivations Scale (PUMS) in a diverse, multicultural sample, and develop a short form (PUMS-8) that can assess a wide range of pornography use motivations. Using data from 42 countries (N = 75,117; Mage = 32.07; SDage = 12.37), enabled us to thoroughly evaluate the dimensionality, validity, and reliability of the Pornography Use Motivations Scale (PUMS), leading to the development of the more concise PUMS-8 short scale. Additionally, language-, nationality-, gender-, and sexual-orientation-based measurement invariance tests were conducted to test the comparability across groups. Both the PUMS and the PUMS-8 assess eight pornography use motivations, and both demonstrated excellent psychometric properties. Sexual Pleasure emerged as the most frequent motivation for pornography use across countries, genders, and sexual orientations, while differences were observed concerning other motivations (e.g. self-exploration was more prevalent among gender-diverse individuals than men or women). The motivational background of pornography use showed high similarity in the examined countries. Both the PUMS and the PUMS-8 are reliable and valid measurement tools to assess different types of motivations for pornography use across countries, genders, and sexual orientations. Both scales are recommended for use in research and clinical settings.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".