Multimodal meaning-making during play in two Northern Canadian Indigenous kindergarten classrooms
Bibliographic record
Abstract
This research explores young Indigenous children’s multimodal meaning-making to carry out social intentions in dramatic and construction/materials play settings. The participants are two teachers and 21 children from two Northern Canadian Indigenous communities. Underpinned by social semiotic theory, the research involves inductive analyses of six videos of children’s play. Our findings show a richness in Indigenous children’s meaning-making, as they used verbal and non-verbal modes to carry out 26 specific social intentions that we grouped into four broad social intention categories: Getting Along, Expressing Emotion/Interest, Satisfying Own Needs and Directing. The social intention carried out most frequently was showing interest in an activity. Participating children were more likely to use non-verbal modes, particularly in construction/materials play contexts. They also combined verbal and non-verbal modes to achieve their social intentions, but did not use verbal modes exclusively. When children used verbal modes to any great extent, it was primarily in a dramatic play context where the teacher took a role in children’s dramatic play. Our research indicates a need for greater attention by educators, curriculum developers and researchers to multimodal meaning-making in Indigenous children’s play, given the cultural importance of non-verbal communication and participating Indigenous children’s remarkable multimodal meaning-making during play.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".