The Canadian Criminal Code Offence of Trafficking in Persons: Challenges from the Field and within the Law
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite early ratification of the United Nations Trafficking in Persons Protocol, the <em>Criminal Code</em> 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 mechanisms and a legal examination of the key components of the offence, we argue that definitional challenges have resulted in narrow understandings and problematic interpretations of the Criminal Code offence. Such narrow interpretations 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 <em>Criminal Code</em> while formulating responses to trafficking that are not dependent on criminal law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it