O13.4 An Ethnographic Mapping Study of “Money Boys” and the Male Sex Trade Industry in Chengdu, South West China
Bibliographic record
Abstract
Background HIV prevalence studies have found that male sex workers (MSWs) in China have high HIV prevalence, ranging from 5% to 9%. These “money boys” have been identified as a particularly vulnerable population, and it is imperative to develop effective programmes for HIV prevention in this population. Objectives The study had 4 objectives: (1) to describe the operation of the male sex work industry; (2) to identify the environmental and structural factors that shape the risk practises of MSWs (3) to map solicitation venues and estimate the size of the MSW population to guide programme design and focus. Method Ethnographic and geo-mapping techniques were combined to locate, enumerate and contextualise different aspects of the male sex trade industry. MSWs, network operators and brothel owners provided size estimates of MSWs for specific hotspots. Participant observation and key informant interviews were employed to understand the risk practises described by participants. Results According to the ethnographic findings, MSWs are often recruited from rural villages via labour markets and from the urban gay community; they had high client volumes and were mobile within the city and between cities throughout China. Thirty MSW venues were mapped and 23 venues were validated. Systematic review and validation of gay websites revealed 48 brothels. Local prevention programmes reached only 16 venues. Five public toilets, 4 bathhouses and 7 parks, where MSWs frequented, were also mapped. The majority of venues were located within the commercial core, with easy access to public transportation. The MSW population was estimated between 373 and 1200. Some MSWs (n = 97) advertised on gay websites and through cell phone applications. Conclusion Despite criminalization, there is a thriving male sex trade in Chengdu. Local efforts to reach these communities need to be scaled up to address programme coverage gaps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".