Lessons from the sex wars: reflections on five decades of the scientific study of pornography
Bibliographic record
Abstract
This article consists of reflections derived from five decades as a research contributor to the scientific study of pornography and its effects. In the article, I note that have learned a lot about classical approaches to the scientific study of pornography, and about what science can – and, importantly, cannot – tell us about pornography and its effects. I have also seen the distorting influence of strongly held beliefs about pornography in creating bias in the conduct, interpretation and reporting of scientific research in this area. The examples I cite are to be regarded as something akin to ‘teaching cases' and do not provide comprehensive state-of-the-art reviews of the scientific literature – but they do provide a cautionary tale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.072 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.020 | 0.051 |
| Insufficient payload (model declined to judge) | 0.005 | 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".