Translanguaging for and as learning with youth from refugee backgrounds
Bibliographic record
Abstract
Although host countries generally integrate refugees into public education, wide-spread and comprehensive understanding of teaching and learning with children and youth who have experienced forced displacement and migration remains an unmet goal within most education systems. This article explores the educational needs of these children and youth, exploring teacher perceptions of and approaches to students’ language and literacy practices. Sharing insights from case study research conducted in one Canadian school, the article discusses how educators at the school drew upon and engaged students’ linguistic resources as key to student learning, relationships and engagement, catalyzing new configurations of language in education. Analyzing these processes through a translanguaging theory of language, the article discusses how teachers and students engaged “translanguaging instinct” and created “translanguaging spaces” (Li, 2018) in their classrooms to support teaching and learning. Finally, the article proposes a three-dimensional matrix for teachers to use in reflecting on language teaching and learning, comprising axes of (1) teacher- and student-initiated translanguaging; (2) planned and spontaneous engagements with translanguaging; and (3) translanguaging as either a scaffold or a resource for learning. Illustrated with examples from practice and elaborated with teacher reflections, the article describes why such approaches are of critical importance in response to circumstances of forced migration and resettlement of vulnerable populations. Findings arising from this work further support and respond to the call for nuanced understanding of how translanguaging practice and pedagogy materialize within situated educational contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".