Designing Critical Multilingual Multiliteracies Projects in Two-Way Immersion Classrooms: Affordances and Impacts on Students
Bibliographic record
Abstract
Language separation policies in two-way bilingual education (TWBE) reflect ideologies of double monolingualism (Heller, 1995) and ignore the sociolinguistic realities of bi/multilingual students (García & Lin, 2017). This case study investigates the design and implementation of collaborative multilingual identity text projects (Prasad, 2018) in a Spanish-English two-way immersion (TWI) school. Identity text pedagogies (Cummins & Early, 2011) that engage bilingual students in creating dual-language multimodal texts have been taken up across a wide variety of contexts. Few studies in the United States, however, have examined how TWI teachers can use multiliteracies pedagogy (New London Group, 1996) with a critical multilingual language awareness (CMLA) focus to move beyond the frame of Spanish-English through the creation of collaborative multilingual and multimodal class books. A thematic analysis of classroom data from our case study demonstrates that implementing critical multilingual multiliteracies projects fostered students’ CMLA while building positive bi/multilingual identities, leveraged students’ linguistic repertoires beyond the language of instruction, and encouraged linguistic risk-taking. This empirical study highlights the possibilities for adopting a collaborative, critical, and creative multilingual multiliteracies approach in TWI settings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".