From silence to academic engagement: How refugee children with disabilities access learning through inclusive ‘artful’ schools in Canada
Bibliographic record
Abstract
Abstract Many newcomer children spend a ‘silent year’ in elementary school classrooms while they adjust to a new culture and language. This often delays inclusion in learning and forming friendships with peers. For refugee children with disabilities (RCDs) this phase may last for 3 years or more, impacting their mental health and sense of belonging, and potentially worsening issues they carry from experiences of war and violence. This paper suggests that these barriers might be overcome through capitalising on strategies that circumvent spoken language by relying on the universal language of art. While making art, children naturally explore their identities, decide how they will present themselves to others, find meaning in a healing narrative and safely process bad memories. The main goal of the study was to uncover hidden ‘knowledge of self and others’ through an arts‐based research approach. Five arts education and art therapy methods with 49 children (aged 7–9) were implemented and evaluated, including self‐portraits, emoji games, read‐aloud story books, paper‐bag puppets and digital stories. Findings reveal that over time, students undergo noticeable changes in their cognitive and affective understandings with exposure to art, and improve their language ability, self‐esteem and well‐being. An unexpected outcome was how the arts may scaffold RCDs into academic learning earlier than expected. Examples of student art are included in Appendix A.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".