Lessons from France on the regulation of Internet pornography: How displacement effects, circumvention, and legislative scope may limit the efficacy of Article 23
Bibliographic record
Abstract
Abstract In 2020, the French Parliament passed an amendment that put the country at the forefront of attempts by democratic states to restrict young people's access to legal online pornography. This study examines the necessity for and potential efficacy of the amendment, Article 23, through a comparative analysis of emerging legislative and regulatory approaches in France, the UK, Canada, Utah, and Germany, and through a survey of French 15‐, 16‐, and 17‐year‐olds. Among other things, our survey shows that 41% of 15‐, 16‐, and 17‐year‐olds in France visit dedicated pornographic sites, on average monthly and often much more frequently. However, the range of media platforms via which French adolescents are exposed to pornography, their knowledge about technologies that could circumvent age verification, and the power, scope, and implementation of Article 23 may limit the legislation's efficacy. Our findings suggest the mechanisms that may limit its efficacy include media displacement, socio‐technical circumvention, and the Article's relatively broad and imprecise nature. This study has implications for legislators and regulators in democratic countries beyond France as they too grapple with the challenges of regulating online pornography. Furthermore, it extends the often contradictory and/or limited evidence that exists about adolescents' consumption of pornography.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".