Sustaining Critical Approaches to Translanguaging in Education: A Contextual Framework
Bibliographic record
Abstract
Abstract Translanguaging remains a timely and important topic in bi/multilingual education. The most recent turn in translanguaging scholarship involves attention to translanguaging in context in response to critiques of translanguaging as a universally empowering educational practice. In this paper, seven early career translanguaging scholars propose a framework for researching translanguaging “in context,” drawing on the Douglas Fir Group's (2016) transdisciplinary framework for language acquisition. Examining translanguaging in context entails paying attention to who in a classroom wields power, as a result of their greater proficiency in societally valued languages, their more “standard” ways of speaking these languages, their greater familiarity with academic literacies valued at school, and/or their more “legitimate” forms of translanguaging. In our framework for researching translanguaging in context, we propose three principles. The first principle is obvious: (1) not to do so apolitically. The other two principles describe a synergy between ethnographic research and teacher‐researcher collaborative research: (2) ethnographic research can assess macro‐level language ideologies and enacted language hegemonies at the micro‐ and meso levels, and (3) teacher‐researcher collaborations must create and sustain inclusive, equitable classroom social orders and alternative academic norms different from the ones documented to occur in context if left by chance.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.118 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".