“I Belong to Nowhere”: Syrian Refugee Children’s Perspectives on School Integration
Bibliographic record
Abstract
Since 2011, the armed conflict that began in the Syrian Arab Republic has displaced an estimated 12 million Syrians, forcing them to seek refuge in various countries around the world. Over half of those uprooted are children. Education is key to integration of refugee children and is considered critical in bringing back a sense of normalcy, routine as well as emotional and social well-being in the lives of refugee children. In Canada, integration of Syrian refugee children in the public school system has, therefore, been identified as one of the vital aspects of their settlement needs. This article examines the challenges experienced by newly arrived Syrian refugee children as they struggle to integrate to the Canadian school system. We have conducted five focus groups with twelve Syrian refugee parents and eighteen Syrian refugee children between the age group of 10-14. Our research shows that Syrian refugee children not only find it difficult to make friends with local students but are also subjected to constant bullying and racism that affect their sense of belonging and connection. Making the views of these students explicit, we hope to provide a starting point for not only understanding their experiences in more detail, but also for developing educational strategies, resources and policies that might best meet the needs of these students and future refugee children and youth.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".