Storytelling through block play: imagining identities and creative citizenship
Bibliographic record
Abstract
Abstract In 2021, more than 80 million people worldwide will have been forced to flee their homes. Upon arrival in their new country, families may endure numerous hardships, yet succumbing to these challenges is not their single story. To understand how migrant‐background and refugee‐background children imagine more liveable futures beyond social and education barriers, financial stress and unresolved emotional issues, our study focuses on the stories that 8‐ to 10‐year‐old learners created while playing with building toys and stacking blocks in a Canadian elementary school. Drawing on the interconnected frameworks of story‐telling, identity, creative citizenship and play‐based pedagogies, our case study of 11 students illustrates that, in response to an invitation to support their real or imagined communities, learners engaged in literacy practices, built on their lived experiences and imagined strong identities to create stories of social responsibility and awareness, emphasising the human needs of securing food and fresh water, ensuring safety, and connecting and caring for the community. Our findings may encourage teachers to consider play‐based storytelling to address out‐of‐school social factors in their classrooms and to capitalise on students' inquiries to design interdisciplinary projects that can develop students' literacies and promote social activism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".