Social service responses to human trafficking: the making of a public health problem
Bibliographic record
Abstract
Human trafficking has received considerable attention from policymakers, researchers and service providers globally, with resulting interventions often positioning trafficking as something that simply exists. Drawing on Bacchi’s ‘What’s the Problem Represented to be?’ approach, this article proposes that trafficking is continually made through efforts designed to eradicate it. We conducted 22 interviews with representatives from social service organisations funded by the government of Ontario, Canada, for anti-trafficking programming. These interviews provide insight into how trafficking is being represented and with what effects. Our findings suggest that organisational initiatives often rely on individualised health-related interventions, such as trauma-informed counselling and other mental health support, to address trafficking. In the process, various sex work activities are deemed ‘symptoms’ of trafficking, and perceived pathways to engaging in sex work (such as drug use/dependence, a history of trauma and low self-esteem) are produced as ‘causes’ or ‘risk factors’. We contend that by pathologising sex work and sex workers, organisations are employing a contradictory neoliberal paternalism to advance a public health representation of human trafficking that simultaneously responsibilises and disenfranchises purported victims.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.057 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".