Communications in sex work : a content analysis of online sex work advertisements among men, women and transgender people in Vancouver
Bibliographic record
Abstract
The increased use of technology to purchase goods and services has changed the landscape of how we advertise, buy and sell commodities. This has contributed to an increase in off-street sex work advertised on the Internet. It is estimated that 80% of sex work in British Columbia occurs off street and the use of web advertising for services has grown exponentially (O’Doherty, 2011). While street-based sex work has been well studied, and there is a significant and growing body of knowledge concerning off-street sex work, communications in advertising sex work online is an emerging field of inquiry. There have been few studies that have examined these communications, and most have been population specific. In this study, 75 online advertisements for sex work in Vancouver, British Columbia were compared to determine what information was regularly communicated and how this information differed between men, women and transgender people using this medium to conduct business. Content analysis was employed as a method to extract the data from the websites in a systemized, categorical way and the results were analyzed to compare differences between groups, focusing on communications, health, safety, and business information. The findings suggest that while there are similarities between men, women and transgender people advertising sex work online, there are important differences that require further study to determine if they have impacted the health and safety of sex workers. This study summarizes what is being communicated in online advertisements of sex workers and contributes to understandings about how sex workers are communicating about health, safety and business to their clients. These insights can assist health care providers and policy makers in creating interventions to improve health and safety for sex workers and their clients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".