The Spaces In Between: Understanding Children’s Creative Expression in Temporary Shelters for Asylum Seekers
Bibliographic record
Abstract
On arrival in a host country, asylum‐seeking children face uncertainty and stress that may compound past traumatic experiences of war and violence. This article is based on a participatory action research project, Welcome Haven, that aims to promote the wellbeing and mental health of asylum‐seeking families in Montreal, Canada, through psychosocial workshops. Since 2023, our interdisciplinary team has conducted arts‐based workshops to support asylum‐seeking children lodged in hotels that function as temporary accommodations, funded by the federal government. This study examines the drawings and narratives of participating children (ages 5–17) to understand how children communicate and make sense of their experiences through artmaking. Following a participatory action research framework using arts‐based approaches, we use narrative and thematic analysis to analyze our (a) ethnographic field notes, (b) notes from our intervention team meetings, which functioned as peer supervision for facilitators, and (c) photographs of children’s artwork. Our findings suggest that children use drawings to share and externalize their personal stories and to express fears and hopes for the future. Importantly, children’s expression happened not only on the page and through stories, but in the space between facilitators and children, and in their manner of sharing or protecting their art. The challenges of conducting research and creating therapeutic alliances in these spaces are explored. This research has important implications for understanding children affected by war and those in humanitarian crisis settings, including reception centers and shelters in high‐income countries.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".