Translanguaging for Transgressive Praxis: Promoting Critical Literacy in a MultiAge Bilingual Classroom
Bibliographic record
Abstract
bell hooks's (1994) advocacy for teaching to transgress invites educators and students alike to transgress boundaries to strive for ways to know and live fully and deeply as whole human beings. The authors aim to showcase a transgressive attempt in bringing French and English into one multiage (Grades 4–6) classroom, with its two teachers—English language arts and French as a second language—coordinating their teaching and curriculum design to build meaningful bridges across content and languages to deepen critical understanding. The project was transgressive in its translanguaging practices that defy dominant monolingual hegemony, challenging traditional bilingual education practices in North America. It was also transgressive in its commitment to the Freirean view of literacy as reading (and writing) the word and the world. Adopting a literature-based curriculum and a critical literacy approach, the teachers engaged their students in exploring concepts recursively in both English and French to deconstruct social stereotypes, promote respect for diversity, and cultivate self-reflexivity regarding complicity in social injustice. Discourse analysis of ethnographic data shows how translanguaging created new possibilities and transgressive spaces for learners to adopt identities of competent bilingual users and critical agents of social change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".