No Country for Young Refugees: Barriers and Opportunities for Inclusive Refugee Education Practices
Bibliographic record
Abstract
The recent refugee crises in Ukraine (2022) and Syria (2011) have created millions of refugees, 40% of whom are children. The education systems of countries hosting refugees struggle to integrate such large populations. In addition, language barriers and the stigma associated with refugees hamper inclusive and equitable education opportunities for these children. There is thus a risk of "lost generations" distanced from education, who may eventually depend on social security systems and monetary aid in the long term. This study considers the following research question: How can a host country improve the inclusion of refugee children in the education system without overburdening its infrastructure? First, we document the availability and accessibility challenges and opportunities that refugee children face during the Syrian refugee crisis. We then develop an inclusive planning strategy aligned with existing capacity and resources and formulate two adaptations of the maximum covering problem (MCP): cooperative capacitated MCP with heterogeneity constraints (CCMCP-HC) to improve the current schooling access in Türkiye and Modular CCMCP-HC to guide early planning in the case of a future crisis. Our computational analyses illustrate that the proposed approach yields higher schooling rates and capacity utilization than existing approaches. Our results emphasize the importance of having a planning strategy in the initial phases of a crisis that considers future integration possibilities. This study analyzes Türkiye's experience and lessons learned to provide a road map for other ongoing and future refugee crises.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".