Variations in the population size, distribution and client volume among female sex workers in seven cities of Pakistan
Bibliographic record
Abstract
OBJECTIVES: To describe the size and distribution of female sex worker (FSW) populations and the distribution of client-FSW encounters in seven major cities of Pakistan. METHODS: Mapping of FSWs was done using a two-stage process of identifying and validating locations where FSWs solicit and/or meet clients, estimating the size of the FSW population at each location and describing the type of sex work. A sample survey of FSWs was conducted to collect data on sociodemographic and behavioural data. Survey data on client volume were analysed to assess the distributional inequality of client sexual encounters in each of these cities. The overall distributional inequality in client-sex worker encounters across the entire FSW population within a city was assessed by drawing Lorenz curves and computing the Gini coefficient. RESULTS: A total of 34 480 FSWs (40% street-based, 57.5% home-based and 2% brothel-based) were mapped in the seven cities. Of these, 2869 participated in behavioural and biological surveys. The median age of FSWs surveyed was 26 years with sexual debut at 18 years. The contribution of different types of FSWs to the total client volume differed substantially between cities, with the contribution of home-based FSWs ranging from 32% to 75%. The overall distributional inequality in client volume also varied substantially between cities, with the Gini coefficient ranging from 0.22 (low inequality) to 0.50 (high inequality). CONCLUSIONS: The relative size and distribution of sex workers and the sex worker-client patterns differs considerably in cities of Pakistan. Programmes should be planned and implemented accordingly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".