The (in)coherence of Canadian refugee education policy with the United Nations’ strategy
Bibliographic record
Abstract
This study assesses the coherence of Canada’s educational policy regime with the United Nations High Commissioner for Refugees’ (UNHCR) Refugee Education 2030 strategy. We articulate a theoretical framework that combines theories about policy coherence, policy attributes, and policy tools, which informs a two-phase methodology. First, we conducted jurisdiction-based scoping reviews of policies in Canada’s 13 provinces and territories which have constitutional authority over education. This yielded a sample of 155 documents, which we then analyzed for its vertical coherence with Refugee Education 2030. Our analysis focused on five categories of need in the UNHCR strategy with respect to refugee students, namely access to education, accelerated education, language education, mental health and psychosocial support, and special education. The findings reveal there are policies across Canada that target responses to the five categories of need. Although some policies are exemplary in their coherence with Refugee Education 2030, Canada’s refugee education policy regime is characterized by many inconsistencies and significant gaps. Policymakers in Canada could use the specific findings to develop or revise policies to address shortcomings. Researchers and policymakers in other countries who find value in our approach could replicate the study’s method in their own jurisdictions, using the instruments provided in appendices to identify strengths and gaps.
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".