Understanding Canadian Responses to Human Trafficking: A comparison of local community and provincial models
Bibliographic record
Abstract
In November of 2000, Canada proclaimed its commitment to prevent, suppress and punish those involved in the trafficking of persons. This research focuses on Canada-based inter-agency anti-trafficking coalitions who focus on responding to human trafficking. Data was collected in the form of a participant-observation study at a three-day conference held in South-western Ontario during the summer of 2015, where seven presentations were provided from four Ontario community coalitions and three provincial approaches from Western Canada regarding their approach to respond to human trafficking. A thematic analysis was conducted to examine each coalition’s approach in responding to human trafficking, with a particular focus on their work against sex trafficking. The secondary goal of this analysis was to compare the approaches used at the community versus provincial levels in Canada. The findings of this research demonstrate that through valued partnerships, Ontario community coalitions work to protect trafficking victims, prevent further instances of trafficking and prosecute traffickers. Lastly, this research demonstrated that community coalitions and provincial models are largely complimentary when responding to human trafficking. In addition to these findings, this research provides a conceptual framework for evaluating community and provincial anti-trafficking approaches to respond to human 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.040 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".