Global Burden of Violence and Other Human Rights Violations Against Sex Workers
Bibliographic record
Abstract
Abstract Globally, sex workers experience a disproportionate burden of violence and human rights violations linked to criminalisation, punitive law enforcement, and lack of labour protections. Social injustices including poor working conditions, violence and victimisation, police harassment, and discrimination constitute severe violations of sex workers’ health, labour and human rights, and abuses of their freedom and dignity. Policymakers, researchers, and international bodies increasingly recognise violence as a critical public health and human rights concern among the general population; however, human rights violations against sex workers remain largely overlooked within international agendas on violence prevention and in human rights conventions. This chapter provides an overview of the global literature on violence against sex workers, other human rights violations, and drivers of elevated violence and rights inequities across settings. In addition to synthesising global research findings, this chapter features contributions and case studies from community partners in Asia Pacific. Guided by a structural determinants framework, and in recognising the right to live and work free from violence as a human right, this chapter provides an evidence base pertaining to violence against sex workers towards that informs the development of policy and public health interventions to uphold human rights among sex workers worldwide.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".