Language abilities of children with refugee backgrounds: Insights from case studies
Bibliographic record
Abstract
Abstract Since 2015, more than 58,000 Syrian refugees have settled in Canada and, at the time of the 2016 national census, more than a fifth had settled in the province of Quebec. The rising numbers of refugees and the risks associated with families’ forced displacement have underscored the need to better understand and support the language of refugee children. The article reports on the oral language of three Syrian children ages five and six years, drawing on data from parent interviews, teacher reports, measures of the children’s language, and observations of their language use in a dual-language stimulation group, StimuLER. By triangulating this data, we were able to develop a rich and realistic portrait of each child’s language abilities. For these three boys, we observed that the home language was vulnerable to delays and weaknesses, and that learning the language of school was a drawn-out process. We also documented that parents and teachers had difficulties communicating with one another, and thus had difficulty meeting the educational needs of these children. We conclude that to foster resiliency in these children who are refugees, schools must find a way to build bridges with the parents to support the children’s language learning in both the language of school and at home.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".