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Record W3011129075 · doi:10.12927/hcpol.2020.26126

Policy Agenda-Setting and Causal Stories: Examining How Organized Interests redefined the Problem of Refugee Health Policy in Canada

2020· article· en· W3011129075 on OpenAlexaffvenueabout
Valentina Antonipillai, Julia Abelson, Olive Wahoush, Andrea Baumann, Lisa Schwartz

Bibliographic record

VenueHealthcare policy · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsHamilton Health SciencesMcMaster UniversityImpact
Fundersnot available
KeywordsRefugeeContext (archaeology)Political sciencePoliticsHealth careHealth policyPublic administrationSociologyLawGeography

Abstract

fetched live from OpenAlex

The development of refugee health policies is significant, given the increased volume of displaced persons seeking refuge in Canada and around the world. Changes to the Canadian refugee health policy, known as the Interim Federal Health Program (IFHP), limited healthcare access for refugees and refugee claimants from 2012 to 2016. In this article, we present a policy analysis using the case of the IFHP retrenchments to examine how political actors on opposing sides of the issue defined the problem using different causal story mechanisms. This analysis reveals that organized interests dramatically changed the problem definition of the IFHP reforms. Following their use of causal stories in redefining the problem, the courts declared that the reforms to refugee healthcare were a form of cruel and unusual treatment. Understanding policy strategies used by proponents of refugee healthcare coverage expansion is important for countries responding to the current, enduring refugee crisis.

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)0.021
metaresearch head score (Gemma)0.042
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: none
Teacher disagreement score0.632
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0460.043
Scholarly communication0.0250.008
Open science0.0040.009
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.372
Teacher spread0.306 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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