Playing the Name Game: A review of advertising practices and success of Canadian sex workers
Bibliographic record
Abstract
Previous studies that considered factors associated with success in sex work used measures such as hourly rates to identify more successful workers. However, such indicators are only an indirect measure of client interest. This study considers a prominent classified advertising venue in Canada that provided statistics on how often ads were viewed, providing a potentially more direct measure of client preferences. Daily views were calculated for a collection of 62582 classified ads generated by 12477 advertisers between July 9, 2023 and August 9, 2023. Factors associated with daily views and language use in ads were considered. During this period, ads were viewed median 128 times per day per ad (IQR 64-248, mean 195, SD 234). Significant findings were that spending more on advertising was not found to result in more daily views. BIPOC advertisers could be associated with more or fewer views compared with White advertisers, with Asian and Black advertisers having fewer views and Hispanic, Middle Eastern, First Nations, and Indo Canadian having more. Male advertisers received ~50% fewer views. Advertisers with large numbers of daily views were much more likely to restrict clients based on race and age than advertisers with fewer daily views. Further work is needed to understand the relationship between ad views and actual ad response. Some assumptions about sex buyers’ race based preferences were shown to be accurate while others were not, suggesting that these preferences are not adequately accounted for by existing theory. Also highlighted is class separation among sex workers, with increased views being associated with greater ability to set boundaries during client interactions.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".