Constructing criminals: a critical frame analysis of Canada’s policy responses to people smuggling and the erosion of refugee protection
Bibliographic record
Abstract
This paper charts the anti-people smuggling policy changes to Canada’s immigration system and uses data since 2010 from government documents, parliamentary speeches and ministerial activity generated by Citizenship and Immigration Canada and related officials. The aim of this study is to demonstrate policy actions and intentions using critical frame analysis to expose underlying narratives and political ideological commitments within the people smuggling discourse. The result shows a constructed understanding of people smuggling as a threat to Canada, and policy violations of international humanitarian obligations according to United Nations protocols. I argue that the current legislation deals with people smuggling through harsh criminalization despite research that shows that the scope, motivations and function of people smuggling vary across time and space. I find that Canada’s anti-people smuggling reform, the Designated Foreign National regime, violates Canada’s international and domestic humanitarian obligations yet is justified by discursive framing of people smuggling under a neoliberal lens that disconnects the phenomenon from humanitarian considerations. I conclude with policy recommendations and areas for future research needed to better understand people smuggling and develop effective, comprehensive rights-based policy responses.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.029 | 0.031 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".