Following the lead of the child: pedagogical considerations to support responsive approaches to play-inspired and playful arts-informed research with young immigrant children
Bibliographic record
Abstract
Despite the proliferation of play-based and arts-informed research, an under-explored area is using both with young immigrant children to hear from them about their distinctive experiences and complexities. This paper draws on an arts-informed and play-based case study with two young immigrant children in Canada to highlight specific data collection approaches, responsive materials, and play and playful art-making pedagogies. The case study found that the use of video recordings, pedagogical improvisations, close observations, and tailored responses enhanced relational co-constructions between the children and the researcher. Responsive materials enhanced the children’s sharing of their thinking, interpretations, and communication and were effective when relevant to the child and acted as a prompt. Establishing a shared studio space for the children and the researcher was important for supporting interwoven opportunities for play and artistic representations. Illustrative examples highlight how these established considerations can be tailored to young immigrant children to help them explore and negotiate complexities including identity and culture, socially and culturally constructed roles, family and community supports, and pre-and post-migration experiences.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".