Surveying Pornography Use: A Shaky Science Resting on Poor Measurement Foundations
Bibliographic record
Abstract
A great deal of pornography research relies on dubious measurements. Measurement of pornography use has been highly variable across studies and existing measurement approaches have not been developed using standard psychometric practices nor have they addressed construct validation or reliability. This state of affairs is problematic for the accumulation of knowledge about the nature of pornography use, its antecedents, correlates, and consequences, as it can contribute to inconsistent results across studies and undermine the generalizability of research findings. This article provides a summary of contemporary measurement practices in pornography research accompanied by an explication of the problems therein. It also offers suggestions on how best to move forward by adopting a more limited set of standardized and validated instruments. We recommend that the creation of such instruments be guided by the careful and thorough conceptualization of pornography use and systematic adherence to measurement development principles.
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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 | 0.048 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".