Sexual Trafficking in the Canadian Context: Exploring the Political Landscape, Examining Discourse, and Identifying Health Issues among Women with Lived Experience
Bibliographic record
Abstract
Although human trafficking has a long history, it has more recently become a topic of profound interest in popular culture, in political debates on sex work and immigration, and among law enforcement and social service providers. However, the widespread interest in human trafficking has not translated into a clear or consistent understanding of the phenomenon or the experiences of those who have been trafficked. This study sought to explore the perspectives of women who have been trafficked for sexual exploitation and the professionals who work with them. Specifically, the study examined three key issues in sexual trafficking: the political and legal climate of sex trafficking in Canada, the discourses on sex trafficking and how it is defined, and the physical and mental health experiences of sexually trafficked women. These topics are explored within the Canadian context with an emphasis on experiences in Southwestern Ontario (where eight of the 12 participants were trafficked or work). Using qualitative research methods, namely semi-structured individual interviews, and approaches informed by critical feminist theory, data was gathered from four participant groups: women who have been sexually trafficked (n=3), service providers who work with sexually trafficked women (n=3), members of law enforcement (n=3), and service providers for other forms of labour trafficking (n=3). Given the lack of data and empirical research on sexually trafficked women, these findings are unique and of direct value to local service providers, policy stakeholders, and women with lived experience.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.047 | 0.022 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".