Redefining Refugees in Canada: Comparing Policy Frames for Refugee Health Care Policy in Canada and the United States
Bibliographic record
Abstract
In 2012 the Government of Canada announced a series of changes to the Interim Federal Health Program responsible for health insurance for refugees and asylum seekers in Canada. The reform restricts medical coverage for certain groups, including privately sponsored refuges and asylum seekers with pending or denied decisions. Despite strong opposition from medical professionals, refugee advocates and provincial governments, the Government defended its decision on the grounds that the previous system was “too generous” and it would be unfair to grant medical insurance coverage beyond that offered to Canadians. Similarly, in the United States the Refugee Medical Assistance Program also distinguishes health coverage eligibility between refugees and asylum seekers, offering comprehensive medical coverage for refugees for an eight month period upon arrival. In this paper I conduct a comparative analysis between Canada and the United States in the area of refugee health care policies. The analysis concludes Canada is moving closer to emulating the existing refugee health policy in the United States. In addition, in this paper I argue that the ways legitimate refugees have been framed in the United States have also influenced recent shifts in the way Canada has narrowed the scope of who qualifies as a legitimate refugee and what state services these persons are eligible to receive.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 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".