The Stories We Tell about Refugee Claimants: Contested Frames of the Health-Care Access Question in Canada
Bibliographic record
Abstract
A contested issue is the extent to which refugee claimants should have access to health care in Western host countries with publicly subsidized health-care systems. In Canada, for a period of over fifty years, the federal government provided relatively comprehensive health coverage to refugees and refugee claimants through the Interim Federal Health Plan (IFHP). Significant cuts to the IFHP were implemented in June 2012 by the Conservative federal government (2006–15), who justified these cuts through public statements portraying refugee claimants as bring- ing bogus claims that inundate the refugee determination system. A markedly different narrative was articulated by a pan-Canadian coalition of health providers who characterized refugee claimants as innocent victims done further harm by inhumane health-care cuts. This article presents an analysis of these two positions in terms of frame theory, with a greater emphasis on the health-provider position. This debate can be meaningfully analyzed as a contest between competing frames: bogus and victim. Frame theory suggests that frames by nature simplify and condense, in this case packaging complex realities about refugee claimants into singular images (bogus and victim), aiming to inspire suspicion and compassion respectively. It will be argued that the acceptance of current frames impoverishes the conversation by reinforcing problematic notions about refugee claimants while also obscuring a rights-based argument for why claimants should have substantial access to health care.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.101 | 0.048 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".