The politics of distraction in planning English-medium education policy in schools
Bibliographic record
Abstract
This article presents the findings of a critical ethnography focused on the English-medium instruction (EMI) policy in Nepal’s public schools. Through the analysis of policy documents and interviews with policymakers, the study reveals that policymakers view the EMI policy as a solution to the crisis in public schools by enhancing their competitiveness with private English-medium schools. However, this approach is identified as a ‘politics of distraction’, as it diverts attention from broader issues such as implicit privatization, funding cuts, and accountability deficits for implementing multilingual education policy. By framing EMI as a public policy doctrine using discursive strategies (e.g. neoliberal rationalization and justification) and suggesting that the crisis can be resolved through school privatization, which in turn promotes commodified languages like English and the national dominant language, Nepali, over local/Indigenous languages, policymakers largely disregard inequalities, structural conditions, and reinforce the existing unequal power relations. By diverting attention from critical issues, policymakers perpetuate historical marginalization, colonial agendas and ideologies, and unequal power asymmetries, failing to address systemic challenges. The research underscores the necessity of scrutinizing the motivations and agendas underlying the promotion of EMI in mainstream schools in multilingual 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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.056 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".