The limits of ‘zero tolerance’ policies for animated pornographic media
Bibliographic record
Abstract
This article examines how policies over animation on pornographic platforms fail to take into consideration its nature as manufactured media, a genre category that now encompasses cartoons and artificially generated moving images. Looking at 30 pornographic platforms' policy documents, it takes as an example the challenging articulation of the highly regulated Child Sexual Abuse Material (CSAM) category when the content is non-photorealistic animation: what compliance issues might uploaders face when moderators apply live-action governance frameworks to animated content? Specifically, the article looks at the consequences of ‘zero tolerance’ arguments used by policymakers that blur together the potential harms existing in either manufactured or live-acted media, without proper distinction between both. From there, it argues that the moderation of animation on pornographic platforms must instead consider that non-photorealistic animated pornographic media emerges from an adult subculture with its own history and inner ethical debates, is inseparable from systemic power dynamics of media representation within the animation industry, and cannot be disassociated from the labour that constitutes its creative force. By demonstrating that current policies do not account for the specific needs that animation asks for, the article argues that platforms are left ill-equipped to moderate non-photorealistic, artificially generated 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 | 0.064 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".