Towards critical translanguaging: a review of literature on English as a medium of instruction in South Asia’s school education
Bibliographic record
Abstract
English as a medium of instruction (EMI) has recently dominated language-in-education policies in South Asia while bilingual/multilingual practices have historically been the norm in primary and secondary education. Although the research on translanguaging is rapidly undertaken in English-only spaces in other world regions (e.g. North America), such studies are rare in South Asia. To this end, this conceptual article focuses on EMI research and policies in South Asia and discusses how the use of multiple languages or translanguaging is practiced in EMI classrooms, what kinds of scholarly judgments are given to such translingual practices, and what ideological limitations exist in translanguaging practices. We show that translanguaging practices are traditional norms in South Asian EMI classrooms but not necessarily a planned pedagogic approach. It is, rather, a spontaneous practice used as a ‘coping strategy’ of English language domination. We also discuss how seeing elite bilingualism (English plus a national dominant language) as translanguaging can be a liberal approach perpetuating unequal language hierarchies in education. Therefore, we argue that ‘critical translanguaging’ should resist nationalist and neoliberal ideologies that position languages and their users unequally, and instead protect the language, culture, and identity of those who have historically received marginalization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".