“Imaginings”: Reflections on Plurilingual Students’ Creative Multimodal Works
Bibliographic record
Abstract
The purpose of this article is to illustrate the potential contribution of a multimodal approach to English language teaching and learning in the educational context. Collaborating with English as a second language (ESL) and classroom teachers to explore ways to improve pedagogy in multilingual, multicultural schools, the authors discovered many teachers who used the creation of multimodal texts as a core instructional strategy to go beyond basic approaches to language teaching and learning. In particular, these teachers used the creation of multimodalidentity texts(Cummins & Early, 2011) as a means to involve students in producing work that was culturally relevant, socially significant, and personally meaningful. To illustrate these possibilities, the article draws on examples of student‐ and teacher‐created multimodal texts that were showcased at a regional conference forESLteachers in Ontario in 2012 and 2014. Through interviews with students and teachers, the authors found that students actively used multimodal resources to represent and articulate personal narratives of themselves, their communities, and their language learning experiences. These narratives reflect students not only as language learners but also, more powerfully, as plurilingual subjects with voice and agency. The authors conclude by reflecting on the potential for plurilingual multimodal production in the English language classroom as a form of teaching for social justice.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.042 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.012 |
| 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".