English-Medium Instruction (EMI) as Linguistic Capital in Nepal: Promises and Realities
Bibliographic record
Abstract
This article reports on a critical qualitative case study of an EMI-based, underresourced public school in Nepal through Bourdieu’s lens of linguistic capital. As the data analysis revealed, parents, students, and teachers regarded EMI as a privileged form of linguistic capital for developing advanced English skills, enhancing educational achievements and access to higher education, and increasing the chance of upward social and economic mobility. In contrary to these rosy perceptions of EMI with overtly superficial promises, switching to EMI, without enough teacher preparation and infrastructure support in the school, had contributed to several unplanned negative outcomes, including a contested process of developing the English proficiency. Despite the school’s claim of offering EMI education, Nepali was the actual language of instruction in the school due to teachers’ lack of proficiency in English and the school’s inadequate resources and preparedness for a shift to EMI. As a result, the students developed neither the content knowledge nor English language skills. Therefore, rather than being an educational equalizer, EMI has served to (re)produce linguistic marginalization and educational inequality and injustice for children from a lower socioeconomic status. We suggest critical reflection on EMI adoption and reenvision “sustainable additive multilingualism” in such contexts (Erling et al., 2016).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".