Problematizing Canadian Human Trafficking Policy
Bibliographic record
Abstract
This thesis seeks to determine how human trafficking is problematized in Canadian policy and what subsequent effects are produced by this problem representation in the lived experience of political subjects in Canada. Using Carol Bacchi’s “What’s the problem represented to be?” poststructural analytic strategy to Canadian policy texts, I demonstrate that human trafficking is represented as a “criminality problem” in Canadian policy texts. The criminality problematization represents the “problem” of human trafficking to be crime requiring state intervention in the form of enforcement and punishment through fees and incarceration. The criminality problematization is predominantly interested in women as potential victims of sex trafficking, and emphasizes strategies to safeguard women from predominantly male violence. Chapters three and four perform Foucauldian archaeology and genealogy, troubling the assumptions which undergird the problematization and revealing the impact of white slavery as a discursive precedent to human trafficking. Chapter five identifies the discursive, subjectification and lived effects of the problematization, revealing that the criminality problematization has served only to reinforce preexisting inequalities, oppressions, and vulnerabilities that create the conditions that lead to human trafficking in the first place. I conclude that the criminality problematization of human trafficking cannot yield socially just policy, and I suggest the “inequality problematization,” as an alternative problem representation that considers human trafficking to be a “problem” rooted in, and exacerbated by, inequality.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.043 | 0.021 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".