Modernization in the Education System of Nepal: Reflections from School Education Sector Plan
Bibliographic record
Abstract
The terms ‘modernization’ and ‘education’ are intertwined. These terms maintain a reciprocal relationship that complements each other, allowing them to grow and contribute to the nation-state’s development. The modernization of any state has a significant impact on its education system. The study aims to explore how the process of modernization has shaped the current education system of Nepal and how the elements of modernization have been incorporated into the School Education Sector Plan (SESP) 2022/23–2031/32. It analyzes the SESP document which outlines the educational plans and policies of Nepal. Simultaneously, the study investigates how these plans and policies contribute to the modernization of the education system. Relevant literatures from books and journals were reviewed to examine the relationship between education and modernization. A qualitative document analysis method was employed to critically analyze the elements of modernization embedded in the objectives, strategies and guidelines of the SESP document. This study reveals that SESP aims to promote equity, inclusivity, digital learning, Technical and Vocational Education and Training (TEVT), the use of Information and Communication Technology (ICT), along with the protection of child rights and healthcare to increase access and ensure quality learning. Additionally, it encourages community participation, implementation of decentralization policy and use of the Integrated Educational Management Information System (IEMIS) in schools to ensure good educational governance and a better school system.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".