Power users: Technology, trust, and the social networks of Canadian sex workers
Bibliographic record
Abstract
The transition from physical to online advertising by sex workers in Canada has been well documented. However, few studies use rigorous sampling methods. This study considers how a technically sophisticated group of advertisers from a large Canadian sex work classifieds site used multiple online resources to promote or provide services during the COVID-19 pandemic. Advertisers qualified for the study if they used a URL as part of their contact information and were actively advertising between August 23 and September 22, 2022. A random sample of 1000 qualifying advertisers were selected of which 783 had accessible contact URLs. Themes were identified in downloaded website texts using grounded theory analysis. Ad metadata was used to identify demographic and behavioral distinctions between the sample and other advertisers. Almost all sampled advertisers (99%) provided in person services and most (70%) provided online services. The sample advertised more frequently, were more affluent and were more likely to be Anglophone, White, trans-female, or provide BDSM services. Themes of security, health, identity, and social networks were identified. Advertisers emphasized physical, emotional, and financial security. Most workers did not work in isolation and many participated in extensive social networks.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".