Porn Vilification and Age Verification: Regulating Online Pornography and Sex Work
Bibliographic record
Abstract
Abstract: This commentary presents regulatory mechanisms recently proposed or passed by countries around the world requiring providers of online pornography to implement age-verification technologies. Canada's "Protecting Young Persons from Exposure to Pornography" Act (PYPEPA) is presented as a key exemplar of the problematic discourses used to construct pornography and sex work as dangerous that contribute to the creation of harmful and ineffective legislation with far-reaching consequences for the sex work community. PYPEPA and other presented legislation demonstrate a pattern of moralistic policies rooted in problematic discourses and fundamental misunderstandings of the sex work industry, which persist despite evidence of their growing harm. This problematic framing of online pornography creates a perceived need for the government to "do something", resulting in punitive policies that have far-reaching consequences. While proponents of these bills are attempting to reduce the potential for harm on children who access to online pornography, the stated goals of the legislation suggest that they are unnecessarily concerned with defining acceptable categories of sexuality. Alternatives to the vilification of online pornography, with the mutually-aligned goal of limiting the potential for harm, are explored as a better way 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".