Translanguaging as a (re)imagining of students of refugee background and trauma
Bibliographic record
Abstract
The purpose of this research is to understand how teacher candidates take-up, critique and push beyond new learning about supporting multilingual students of refugee background in the spaces where they are learning to teach. This qualitative case study design combines observations, interviews, and document analysis across three sections of a required course designed to prepare teacher candidates to work with multilingual students. The theoretical perspective of translanguaging and anti-racist approaches to trauma inform this research. Findings suggest that teacher candidates framed children of refugee background as limited, traumatised, and having behaviour problems. However, in language-rich environments and moments on their practicum when working directly with the children, the candidates described translanguaging as a place of belonging that allowed for the building of stronger relationships and seeing the students’ existing literacy practices as assets.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| 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".