Elementary Schooling Across Borders: Refugee-Background Children’s Pre- and Post-Migration Experiences
Bibliographic record
Abstract
In response to the ongoing war in Syria, displaced persons fleeing war and persecution transited in neighboring countries before going on to resettle permanently in countries of reception such as Canada. The overall purpose of the study reported on here was to inquire into Syrian refugee background children’s early educational experiences in Syria, in transit countries, and in Canadian elementary schools. There is very limited scholarship on young refugee-background children’s schooling from their own perspectives across contexts, yet elements of these experiences have broader implications for understanding educational gaps and provisions. Theoretically framed by hermeneutics, in this qualitative interpretive inquiry, artistic and interview data were collected from eight Syrian children from refugee backgrounds, their parents, and their teachers over the period of a year. Data were analyzed within the hermeneutic circle following two arcs structuring iterative movement between our preliminary interpretations and interrogations of these understandings. Educational loss, safety, and resources and supports are elaborated as themes across contexts to exemplify assets the participants accrued in spite of adverse circumstances. Implications for constructing inclusive spaces for children from refugee backgrounds enrolled in elementary schools will be discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".