Navigating the Complexities of the Education of Students with a Refugee Background: A Continuum of Adaptation
Bibliographic record
Abstract
Recent conflicts have contributed to a large influx of refugees to Europe and North America. These displacements, combined with global tensions, have led to restrictive migration policies. Despite these challenges, several countries pledged in 2016 to integrate students of refugee background into national education systems, guaranteeing them access to quality education soon after arrival. This article examines refugee education in Western countries in terms of availability, accessibility, acceptability and adaptability, focusing on Canada, in particular Ontario, and comparing it with practices in some European countries. The study highlights the significant variability of refugee education within countries and questions the legitimacy of reception classes in relation to mainstream classes. The article highlights the need for nuanced approaches that take into account individual migration trajectories, diverse educational needs, and systemic challenges inherent to each educational system. It advocates for a shift from deficit-based to strength-based pedagogical practices, while emphasizing the importance of adaptability in education, supported by strong leadership within school teams. The Canadian example illustrates both the potential for inclusion and the societal challenges.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.006 |
| 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".