Bibliographic record
Abstract
This chapter is about the local health officials who implement China’s surveillance and behavioral outreach health policies for estimating the prevalence of HIV/AIDS and reducing its occurrence among sex workers. These policies set out clear guidelines for targeting certain types and numbers of sex workers for HIV/AIDS testing and outreach, with the goal of obtaining accurate knowledge of the overall sex worker population and reaching out to the individuals who present the greatest concerns to public health. These policies are also designed to protect the individual rights of sex workers, a prerequisite for obtaining higher quality data and increasing the likelihood that public health interventions will yield safer sexual behaviors. Yet frontline health workers often deviate from these rules, as obstacles within China’s health bureaucracy complicate proper policy implementation. Local health officials must also contend with two powerful entities that are predisposed to oppose their work: the sex industry and the police. Taken together, these challenges lead health agents to focus their testing and outreach efforts on hostesses instead of low-tier sex workers – even though women in the low tier are most in need of health interventions – and result in other irregularities in policy implementation with grave public health consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.219 | 0.106 |
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".