Digital Exclusion and the Structural Barriers to Safety Strategies among Men and Non-Binary Sex Workers Who Solicit Clients Online
Bibliographic record
Abstract
Background: Evidence shows that online solicitation facilitates sex workers’ ability to mitigate the risk of workplace violence. However, little is known about how end-demand sex work criminalization and the regulation of online sex work sites shape men and non-binary sex workers’ ability to maintain their own safety while soliciting services online. Methods: We conducted 21 semi-structured interviews with men and non-binary sex workers in British Columbia between 2020–2021 and examined their ability to enact safety strategies online in the context of end-demand criminalization. Analysis drew on a structural determinants of health framework. Results: Most participants emphasized that sex work is not inherently dangerous and described how soliciting services online facilitated their ability to enact personal safety strategies and remain in control of client interactions. However, participants also described how end-demand criminalization, sex work stigma, and restrictive website policies compromise their ability to solicit services online and to enact safety strategies. Conclusions: Alongside calls to decriminalize sex work, these findings emphasize the need to normalize sex work as a form of labour, promote access to online solicitation among men and non-binary sex workers, and develop standards for online sex work platforms in partnership with sex workers that prioritize sex worker safety.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".