Imagining markets and crafting value: the emergence of an AI-generated pornographic content ecosystem
Bibliographic record
Abstract
This article analyses the economic implications of artificial intelligence (AI)-generated content within the pornographic industries. Much of the recent research has focused on specific issues, for instance the development of deepfakes and their daunting effects. While those are critical questions, this article focuses on better understanding how and why AI-generated pornographic contents find a growing space in online markets, despite their actual and potential adverse effects on numerous people. We investigate these questions by mapping the ecosystem surrounding the generation and distribution of such content. Our research is built upon a dataset including the documented observation of websites, whether offering such content or linked to them. This also mobilizes content and discourse analysis, mostly drawn on several specialized online websites and forums, and is complemented with trade show observation. The article shows how AI-generated pornographic content inserts into the existing and current dynamics of the pornographic industries, where digital platforms and intermediaries intervene between sex workers and content creators on the one side, and customers on the other. To that end, these contents appear as a renewed way for digital intermediaries to extend profitability and aim at a lowered production cost, despite the potential increased social cost.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".