Digitally Facilitated Sex Work: A Scoping Review Articulating Men’s Labor Experiences
Bibliographic record
Abstract
A multitude of factors shape the labor conditions of men engaged in digitally facilitated sex work. To examine these labor conditions, we conducted a scoping review of research conducted with men about their use of internet technologies to facilitate in-person sex work and/or provide sexual services online through digital platforms. We retrieved 72 papers and book chapters published between 1990 and 2024. We summarize some descriptive characteristics and organize the findings according to six working conditions: entry into sex work, advertising and marketing, screening and communications, pay, occupational health and safety, and resources and support. We found primarily qualitative studies examining a variety of sex work sectors and contexts, including a growing body of work about webcamming and porn production. Articles focused on motivations, the role of internet platforms in shaping sex worker practice and identities, marketing and safety strategies, and sexual and community health. Literature increasingly frames sex work in terms of labor and addresses the social, legal, technological, and structural forces that shape sex work conditions. By organizing the findings of existing studies according to labor outcomes and implications, this review aims to further support and facilitate the adoption of a workers' rights perspective within sex work research.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".