Creating Love Letters to Nature: A Case Study of Children’s Multimodal Literacy Practices
Bibliographic record
Abstract
Canada welcomes large numbers of immigrants each year, including children. It is certainly important to understand immigrant children’s educational experience beyond standardized tests in reading and math. This paper draws on a sociocultural approach by situating language and literacy learning in social and cultural contexts and by emphasizing the active role of learners in different contexts. Specifically, the multiliteracies framework (The New London Group, 1996) is used to understand how culturally and linguistically diverse (CLD) children choose to use different literacies and modes to make sense of their surroundings and to create artistic texts to express their understandings of nature, such as water and forests. A qualitative case study was conducted to understand five CLD children’s meaning-making process in a community setting. Data was collected through observations, informal conversations, semi-structured interviews and artifacts. The initial findings of the study indicate that CLD children are active and creative meaning-makers who select different linguistic, cultural and artistic resources as well as various modalities to effectively express their ideas and perspectives according to audience, purpose and context. The presentation discusses two nature projects and shares the artwork of the participating children to highlight a range of multilingual, multicultural and multimodal literacy practices.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".