Identification of Domestically Sex Trafficked Persons in Social Service Settings in Canada: A Qualitative Study
Bibliographic record
Abstract
Social service providers are critical in supporting domestically sex trafficked persons. However, little is known about how these providers identify sex trafficked persons. This study aimed to explore this vital but poorly understood first step to addressing sex trafficked clients’ needs, asking: How do social service providers in Ontario, Canada identify domestically sex trafficked adolescents and adults? Fifteen semi-structured interviews were conducted with diverse social service providers from across Ontario. Participants’ responses to open-ended questions were then analyzed thematically. Findings revealed that providers recognize sex trafficked persons using both commonly reported as well as unique indicators suggestive of sex trafficking, drawing on existing tools and skills developed through or adapted from work with other service populations. However, few had access to formalized practices or resources within their workplace to aid in identifying sex trafficked persons specifically and, as such, many relied on ad hoc processes. While resourceful, this approach can unintentionally perpetuate myths and stereotypes about sex trafficked persons and contribute to missed opportunities for identification. To better support sex trafficked persons, it is recommended that social service providers are provided with tailored training and resources related to identification while adopting and using reflexivity in their everyday practice to combat unconscious biases, beliefs, and attitudes. The work undertaken by social service providers could also be enhanced by knowledge gained from future research designed to evaluate the utility of the sex trafficking indicators and processes for identification described.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".