A Formidable Task: Reflections on obtaining legal empirical evidence on human trafficking in Canada
Bibliographic record
Abstract
This article explores the experiences, challenges and findings of two empirical research studies examining Canada’s legal efforts to combat human trafficking. The authors outline the methodologies of their respective studies and reflect on some of the difficulties they faced in obtaining empirical data on human trafficking court cases and legal proceedings. Ultimately, the authors found that Canadian trafficking case law developments are in their early stages with very few convictions, despite a growing number of police-reported charges. The authors assert it is difficult to assess the efficacy and effects of Canadian anti-trafficking laws and policies due to the institutional and political limitations to collecting legal data in this highly politicised subject area. They conclude with five recommendations to increase the transparency of Canada’s public claims about its anti-trafficking enforcement efforts and call for more empirically-based law reform.
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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 | 0.052 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.035 | 0.026 |
| Scholarly communication | 0.028 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".