English medium instruction in South Asia’s multilingual schools: unpacking the dynamics of ideological orientations, policy/practices, and democratic questions
Bibliographic record
Abstract
This article provides a critical review of English medium instruction (EMI) policy/practices in the K-12 multilingual schools in South Asia, especially in Nepal, India, and Pakistan. Employing Bourdieu’s (1993) lens of ‘linguistic capital’ and ‘linguistic marketplace,’ the article takes stock of (a) the development of EMI and its ideological and pedagogic motivations, (b) the models of EMI policy in relation to mother-tongue-based multilingual education (MTB-MLE) and their practices, and (c) social justice concerns that arise from such policies/practices. As the review of research and policy/practices reveals, EMI is ideologically perceived as a means of acquiring the linguistic capital, often believed to provide access to the global economy; and, therefore, a liberating tool for socioeconomically minoritized groups. Such ideology has, then, oriented the concerned bodies to position EMI within the framework of MTB-MLE in South Asian countries, creating the discourse of inequality and injustice for different social groups. The article continues the argument that the language policies, which are being developed/practiced in the lure of economic globalization, ignoring the local realities, become a source of marginalization along the lines of class, ethnicity, gender, and regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".