Between Law and Stigma: Towards a Human Rights Framework for Sex Work in China
Bibliographic record
Abstract
China's current legal framework--including the Law on the Protection of Rights and Interests of Women, the Law on Public Security Administration Punishments, and the Law on Administrative Punishments—adopts a punitive approach to sex work, systematically violating the fundamental human rights of sex workers. Specifically, sex workers face frequent violence from clients, criminals, and even law enforcement, yet are unable to seek legal protection due to the fear of penalties associated with prostitution. Police often exhibit discriminatory attitudes, engage in violent practices to coerce confessions, and use humiliating enforcement measures. These practices severely compromise sex workers' rights to personal safety, health protection, labour rights, freedom from arbitrary detention, and protection against discrimination. In contrast, countries such as the United States (Nevada), New Zealand and Canada have implemented more human rights-oriented models by legalizing or decriminalizing sex work, effectively reducing stigma, violence, and health risks. China could gradually adopt these international experiences, shifting towards a human rights-centered legal and policy framework.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".