Heterogeneity of characteristics, structure, and dynamics of male and<i>hijra</i>sex workers in selected cities of Pakistan
Bibliographic record
Abstract
BACKGROUND: We sought to describe the characteristics and operational dynamics of male sex workers (MSW) and hijra sex workers (HSWs) in 11 cities across Pakistan in 2011. METHODS: We report descriptive statistics of self-reported sexual behaviour data from cross-sectional mapping and biological and behavioural surveys conducted among 1431 MSWs and 1415 HSWs in four cities across Pakistan in 2011. RESULTS: While Karachi had the largest numbers of MSWs and HSWs, Quetta had the largest relative population sizes, with 3.6 MSWs per 1000 male adults and 3.3 HSWs per 1000 male adults. There was considerable variability in the proportion of HSWs who operate through deras, ranging from 2.2% in Peshawar to 62.7% in Karachi. The number of HSWs per guru varies by city, from 1.5 in Quetta to 16.5 HSWs per guru in Karachi. Among HSWs, the use of mobile phones for solicitation ranged from 37.6% in Quetta to 83% in Peshawar and among MSWs the use of mobile phones ranged from 27% in Karachi to 52% in Quetta. In Quetta, a large proportion of HSWs (41%) find clients through gurus. Client volume tended to be higher among HSWs and among both MSWs and HSWs in Quetta and Peshawar. Condom use with clients was most consistent in Quetta, with 31% of MSWs and 41% of HSWs reporting always using condoms with clients. Peshawar had the greatest proportion reporting never using condoms. CONCLUSIONS: There is considerable geographic heterogeneity in the characteristics and operational dynamics of MSWs and HSWs across Pakistan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".