Elite appropriation of English as a medium of instruction policy and epistemic inequalities in Himalayan schools
Bibliographic record
Abstract
This study reports on an investigation into the perspectives of different stakeholders (e.g. administrators, teachers, students, and parents) towards motivations for introducing English as a medium of instruction (EMI) policy in low-resourced public schools, serving minoritized students, and language ideologies that form its practices. Framed within the notions of neoliberalism and elite bi/multilingualism, this study provides a nuanced understanding of ideological and implementational discourses of the EMI policy in the K-1211 In Nepal, the school education system refers to education from Kindergarten to grade 12. The system includes ‘Early Childhood Education’, ‘Basic Education’ (grades 1 to 8), and ‘Secondary Education’ (grades 9 to 12). Secondary schools often run classes from Kindergarten to grade12 in Nepal. context, which contributes to the emerging field of EMI. As the analysis of interviews and focus groups with the above stakeholders from five different schools in Mt. Everest region and the Kathmandu Valley of Nepal reveals, the key motivations for EMI were to help students gain social and material (economic) capital as EMI was perceived as a means to achieve English skills and quality education. However, such desires, guided by neoliberal logics, have put the minoritized students under delusion because the insufficiency of English proficiency among both teachers and students and the lack of rudiments to effectively implement EMI have created a ‘comprehension crisis’ and ‘epistemic inequalities’ for minoritized students. The findings also illustrate how neoliberal ideologies have led to the practice of elite bilingualism in EMI classrooms, also influencing the local language ecology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".