Bibliographic record
Abstract
Nepal has never been formally colonized; however, it struggles with the intricate effects of both internal and foreign linguistic colonialism, which has resulted in the marginalization of several indigenous languages in the formal educational system. The present paper explores how language education policies are changing in Nepal, a country with a wide variety of languages. This qualitative study used phenomenology as a research method and purposively selected four government aided school teachers as participants. Tool for data collection was interview and the findings showed that English is a dominant language in education policy though the constitution of Nepal allows mother tongue or national language i. e. Nepali to be the medium of instruction in the government schools. The study explored the expanding decolonization movement in language in education policy, led by communities, educators, and grassroots activists. The goal of this movement is to establish a more fair and inclusive learning environment from supporting the acknowledgement and integration of indigenous languages in formal education. The paper explored the historical mechanisms of linguistic colonialism in the educational system, examining the prioritization of dominant languages over indigenous languages and the resultant exclusion of the latter. The study concludes by outlining the current changes being made to Nepal’s language education regulations and highlighting the importance of linguistic inclusion as a driver of social cohesion and cultural preservation. By providing insights into the problems and possibilities of incorporating indigenous languages into formal educational systems, the research adds to the larger conversation on decolonizing education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".