Article The Canadian Criminal Code Offence of Trafficking in Persons: Challenges
Bibliographic record
Abstract
Despite early ratification of the United Nations Trafficking in Persons Protocol, the Criminal Code offence of trafficking in persons in Canada has received little analytical or interpretive attention to date. Adopted in 2005, this offence has resulted in successful convictions in a limited number of cases and criminal justice authorities have continued to rely on alternate or complementary charges in cases of human trafficking. In particular, prosecutions for cases involving non-sexual labour trafficking remain extremely low. This article provides a socio-legal examination of why the offence of trafficking in persons in Canada is under-utilized in labour trafficking cases. Based on an analysis of data generated from 56 one-on-one interviews gathered from a variety of actors involved in counter trafficking response mecha-nisms and a legal examination of the key components of the offence, we argue that definitional challenges have result-ed in narrow understandings and problematic interpretations of the Criminal Code offence. Such narrow interpreta-tions have resulted in restricted applicability, particularly in cases of labour trafficking. More broadly, the article points to the need to address the limitations of the Criminal Code while formulating responses to trafficking that are not depend-ent on criminal law.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".