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Record W7042886593

Redefining Refugees in Canada: Comparing Policy Frames for Refugee Health Care Policy in Canada and the United States

2014· other· en· W7042886593 on OpenAlexaboutno aff

Bibliographic record

VenueuO Research (University of Ottawa) · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)PretextHyporeflexiaPopulationNucleofectionLiquation
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.729

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.011
Science and technology studies0.0200.008
Scholarly communication0.0120.002
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.035
GPT teacher head0.320
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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