Human Trafficking: Canadian Government Policy and Practice
Bibliographic record
Abstract
A recent study undertaken by the authors (Oxman-Martinez and Martinez 2000) examined the Canadian government’s response to the traffic of human beings. Information from twenty-one government and ngo informants and a thorough review of state agency policies and international conventions revealed that trafficking and refugee movements have many links. The question raised by the Metropolis Project—Is clandestine entry to Canada a crime or a new form of migration?—is important, given that trafficking may be the only option available to legitimate refugees waiting to escape dangerous or oppressive situations. Rather than seeking to ease the migration of refugees or addressing the structural causes of trafficking or its social implications, however, Canada’s response is focused on the prevention of “irregular movements” (through immigration and border control) and prosecution of the few traffickers successfully apprehended. Preliminary evidence suggests that border control will fail to adequately address the exploitation of women, children, and men—often refugees— within our frontiers. The temporarily defunct Bill c- 31 (with its measures to control the borders and restrict immigration) threatens the rights of refugees while doing little to prevent human trafficking, protect its victims, or prosecute those who profit from trafficking.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".