Combating human trafficking in the sex trade: can sex workers do it better?
Bibliographic record
Abstract
BACKGROUND: The dominant anti-trafficking paradigm conflates trafficking and sex work, denying evidence that most sex workers choose their profession and justifying police actions that disrupt communities, drive sex workers underground and increase vulnerability. METHODS: We review an alternative response to combating human trafficking and child prostitution in the sex trade, the self-regulatory board (SRB) developed by Durbar Mahila Samanwaya Committee (DMSC, Sonagachi). RESULTS: DMSC-led interventions to remove minors and unwilling women from sex work account for over 80% of successful 'rescues' reported in West Bengal. From 2009 through 2011, 2195 women and girls were screened by SRBs: 170 (7.7%) minors and 45 (2.1%) unwilling adult women were assisted and followed up. The remaining 90.2% received counselling, health care and the option to join savings schemes and other community programmes designed to reduce sex worker vulnerability. Between 1992 and 2011 the proportion of minors in sex work in Sonagachi declined from 25 to 2%. CONCLUSIONS: With its universal surveillance of sex workers entering the profession, attention to rapid and confidential intervention and case management, and primary prevention of trafficking-including microcredit and educational programmes for children of sex workers-the SRB approach stands as a new model of success in anti-trafficking work.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".