Analyzing the talking book Imagine a world: A multimodal approach to English language learning in a multilingual context
Bibliographic record
Abstract
Abstract In recent years there has been increased academic and professional interest and awareness in approaches to English language teaching (ELT) that take a plurilingual approach. This is often combined with a multimodal stance. The outcome of this combination is an approach to English language teaching that integrates multiple languages and multiple semiotic resources. This paper examines how a plurilingual approach to ELT can be viewed through a multimodal lens by analyzing the construction of a plurilingual talking book created as a student project in an elementary public school. The analysis uses multimodal analysis software to map the interaction of languages and images, in order to determine how these function as meaning-making resources in a multimodal, multiple-language text created by linguistically diverse students with high ELT needs. The findings indicate how combinations of different semiotic resources work together to create meaning, delineates the role of English in meaning-making, and illustrates the children’s multilingual interactions in the creation of their collaboratively composed multimodal talking book.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".