Overview and Evidence-Based Recommendations to Address Health and Human Rights Inequities Faced by Sex Workers
Bibliographic record
Abstract
Abstract This volume uses community case studies and data from around the world to highlight the sustained health and social inequities that sex workers in all of their diversity experience in 2020. Guided by a balanced community–academic partnership, this volume aims to ensure that sex workers’ voices are amplified in describing both challenges and the ways forward. Collectively, the chapters describe an elevated burden of HIV, sexually transmitted infections, drug-related harms, violence and other human rights violations, and significant unmet sexual and reproductive health needs. They also demonstrate that sex workers are not passive recipients of such inequity, but rather actively resist and continue to mobilise to advocate for improved health, safety, and human rights conditions and policy changes. Evidence-based recommendations include sex work decriminalisation, ensuring accessible and sex worker-friendly services, removal of punitive policing and surveillance, community empowerment, and strengthening capacity for community engagement in research, policy, and programmes.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".