Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it