‘This is fucking nuts’: the role of payment intermediaries in structuring precarity and dependencies in platformized sex work
Bibliographic record
Abstract
This article explores the impact of governance developed and enforced by payment intermediaries on the working conditions of performers working on adult labour platforms. At the level of the payment infastructure, credit card networks Visa and Mastercard and payment processors set requirements for (adult) platforms on allowable content and how this should be moderated and verified. These rules are subsequently implemented by adult platforms. Based on interviews with 16 industry insiders, fieldwork, document analysis, and a survey amongst 117 online sex workers, the article demonstrates the impact of development and enforcement of these rules on sex workers. We argue that the rules set by payment intermediaries structure the industry in a way that prioritizes the interests of platforms over performers. These dynamics reinforce the already unequal labour relationship between platforms and performers, both by increasing the dependencies of performers on adult platforms and by creating content guidelines, content moderation, and consent verification systems that defer the risks onto performers. Sex workers experience substantial (financial) uncertainty and precarity, which is structured unequally. In particular, they contend with reduced opportunities for stable income, exacerbated health conditions, and increased reliance on third parties and undesired forms of work.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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".