Pornhub and Policy: Examining the Erasure of Pornography Workers in Canadian Platform Governance
Bibliographic record
Abstract
Background: In 2021, the Canadian Parliamentary Standing Committee on Access to Information, Privacy and Ethics (ETHI) conducted an inquiry around Pornhub, following allegations that parent company MindGeek profits from non-consensual content. Analysis: This article offers a discourse analysis of the ETHI’s process, testimony, and report on Pornhub using Carol Bacchi’s policy analysis method, “What is the problem represented to be?” Conclusions and implications: This study reveals a policy process blatantly influenced by anti-porn sentiments, resulting in hearings that framed porn as sexual violence rather than sex industry labour. It exposes how ETHI’s approach failed to constructively engage existing regulations, precarious labour conditions, or platform operations. The result is ineffective policy recommendations that procedurally exclude relevant stakeholders and do not adequately protect platform users from harm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".