Exploring Dropouts as Challenges in Higher Education in Nepal: A Comprehensive Review
Bibliographic record
Abstract
The purpose of this study is to analyse the dropout rate of campus level of students in Nepal. The researchers adopted the process of scientific review as a meta-synthesis to analyse the dropout rate of the campus level students. An in-depth archival analysis followed by an intensive review would be strategies adopted during the scientific review. Secondary data was gathered by searching Google for scholarly publications and articles published between 2001 and 2024. The students want part-time jobs during the study. Such opportunities are less common in Nepal. The research study revealed that Nepali students gave their first priority to Australia, Canada, the USA, and Europe for their higher studies. After finishing their studies, they desired to stay there due to their job security. They applied for green card and permanently stayed there. On the other hand, Nepal lost young and skilled manpower who stayed abroad as immigrants. The study reveals that government can prevent brain-drain by proving financial assistance and employment opportunities to bachelor’s level students. The Nepalese government should make appropriate policy to retain its young workforce. Otherwise, its adverse impact will be seen soon.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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 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".