Alternative Fact? More Democratic States Are More Likely to Provide Refugee Protection
Bibliographic record
Abstract
Democracy is explicitly engaged in two aspects of the Canadian refugee determination process: state protection findings and Designated Country of Origin determinations. Democracy is also implicitly engaged in the selection of countries as so-called “safe countries.” This article reviews the literature on measuring the level of democracy in a given state, and the empirical evidence linking this level to a state’s willingness and ability to provide adequate protection to its citizens. The article argues that the Federal Court of Appeal was misguided in taking judicial notice of a correlation between the level of democracy in a given state and its ability to provide state protection. The article also reviews and questions the use of “democratic governance” as a factor in Immigration, Refugees and Citizenship Canada’s Designated Country of Origin regime, as well as the implicit use of democracy in designating the United States as a “safe” country under the Safe Third Country Agreement. The article contends that the time has come to reconsider how democracy measurements are used in Canada’s refugee determination process, and advocates for an individualized approach to state protection determinations: one that eschews the alternative fact presumption of a connection between democracy and protection, and instead focuses on the protective mechanisms available to a refugee claimant based on their unique circumstances.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".