“Safe” Designations for Unsafe Countries: Security Discourses and the Construction of the Mexican Refugee Applicant "Threat" in Canada
Bibliographic record
Abstract
The Designated Countries of Origin (DCO) Policy was implemented to deter "bogus" refugee claims from "safe" countries.As a result, this thesis questions how Mexico's designation on the DCO policy is justified by the official stance, or the Canadian Government, its actors and institutions.I engage with theorists of Critical Security Studies (CSS) to conduct a discourse analysis of official government documents, speeches, data and case decisions to analyze Mexico's designation.I argue that Mexico's designation as a "safe" DCO country aims to significantly limit Mexican refugee applicants from seeking refuge in Canada.The official stance has constructed Mexico as a "safe" country, Mexican refugee claims as "bogus" and the presence of Mexican refugee applicants in Canada as a "threat" to society.The official stance's use of orthodox security discourses unjustly labels Mexican refugee applicants, eclipsing their personal narratives and restricting their ability to successfully obtain refuge in Canada.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.052 | 0.039 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".