Escorts Online: Effects of Policy on Sex Work through Digital Spaces
Bibliographic record
Abstract
In April 2018, the Trump Administration approved two acts that frame sex work as human trafficking. Subsequently, the Federal Bureau of Investigation seized popular adult personals site, Backpage.com, used by Canadian and American sex workers. This led to the increase in censorship of online spaces, which sex workers require to safely conduct their independent business through advertising, processing secure transactions, and maintaining safe communication with clients and the sex work community. My work aims to understand how these changes have explicitly impacted sex workers as they advertise and communicate their services, working predominantly as escorts in Canada and the United States. Governmental documents and laws like Bill C-36/PCEPA and SESTA/FOSTA, which claim to save exploited populations, may harm autonomous citizens making a living through stigmatized labour by seizing their resources and forcing them to use outdated, unsafe methods of business and communication. Most, if not all, of these regulations are developed without the input of sex workers or relevant empirical evidence. Through interviews with current sex workers in Southern Ontario and an overview of ads, a deeper understanding of the communication practices of consensual sex work, its fight for decriminalization, and the importance of the Internet is reached.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.003 |
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".