Ethnography at an Intersection: Law, Anti-Trafficking NGOs, and Prostitution in India
Bibliographic record
Abstract
For a legal anthropologist interested in how different agents and forms of governance shape projects of sexual humanitarianism, the strategies that US-funded non-governmental organizations (NGOs) use in their attempts to reframe an Indian anti-prostitution law as an anti-trafficking instrument generate broader conceptual questions. How do Indian NGOs articulate donor-driven concerns with the postcolonial socio-legal framework within which they work? What impact do they seek to have on the law, legal system, and legal actors? What, in turn, happens to formal law, which is already shaped by a complex history of legal concerns, moral panics, and NGO intervention (itself authorized by law) in this context? How do law and NGOs shape each other across anti-prostitution and anti-trafficking projects in the overlapping contexts of postcoloniality, globalization, neoliberalism, and sexual humanitarianism in India? How might one explore these intersections and relationships methodologically? I show how ethnography at the intersection of anti-prostitution law and anti-trafficking NGOs illuminates: (1) law’s imbrication in a broader, long-standing, and ever-expanding field of governmental action on prostitution; (2) how NGOs and legal actors act, in tension and in collaboration, upon the perceived “problem” of prostitution; and (3) how anti-trafficking NGOs and anti-prostitution law co-constitute each other as they shape contested meanings around prostitution.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".