Bibliographic record
Abstract
How technology transformed the nature of sex work The internet has revolutionized sex work perhaps more than any other profession. Today’s sex workers go online to attract clients, shape personas, share information, screen potential clients, and build community. The Rise of Digital Sex Work is an intimate look into the changing face of the industry, telling the stories of workers themselves and revealing how they use the internet to share information, grow their businesses, and establish global communities. Kurt Fowler takes us inside the lives of sex workers who provide a variety of services: web-camming, dominatrix work, burlesque, and escorting. He provides insight into how race, class, and privilege affect their work and the role the internet has played in their professional journeys. Drawing on in-depth interviews with fifty workers from the United States, England, Canada, Germany, Australia, South Africa, and other industrialized countries, Fowler explores how they first entered the profession, how they manage their daily business and client relationships, their use of digital technology for safety and as a broader social resource, the role race plays in their work, and how they view their own level of risk and that of fellow sex workers. Fowler provides a look inside sex workers’ digital worlds, as well as the complex meanings they attach to their experiences in their line of work.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".