How language environment, age, and cognitive capacity support the bilingual development of Syrian refugee children recently arrived in Canada
Bibliographic record
Abstract
Abstract Research on the bilingual development of refugee children is limited, despite this group having distinct characteristics and migration experiences that could impact language development. This study examined the role of language environment factors, alongside age and cognitive factors, in shaping the Arabic as a first/heritage language and English as a second language of recently arrived Syrian refugee children in Canada ( N = 133; mean age = 9 years old; mean family residency = 23 months). We found that Arabic was the primary home language with some English use among siblings. Children did not engage frequently in language-rich activities in either language, especially not literacy activities in Arabic. Parent education levels were low: most had primary school only. Hierarchical regression models revealed that stronger nonverbal reasoning skills, more exposure to English at school, more sibling interaction in English, more frequent engagement in language-rich activities in English, and higher maternal and paternal education were associated with larger English vocabularies and greater accuracy with verb morphology. Arabic vocabulary and morphological abilities were predicted by older age (i.e., more first/heritage language exposure), stronger nonverbal reasoning skills and maternal education. We conclude that proximal environment factors, like language use at home and richness, accounted for more variance in the second language than the first/heritage language, but parent factors accounted for variance in both languages.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".