Examining the Connection between Quality Education and Employability: Insights from Educators and Individuals
Bibliographic record
Abstract
The widespread notion, among both the public and scholars, is that developing countries like Nepal experience disparities in educational quality. Issues are regularly flagged in this regard at the policy level, academia, media, and public forums. Nevertheless, there is no research-based discourse about what “quality” means and how it is designed and implemented in different domains. In the case of Nepal, which this article focuses on, it is often presumed that education in public schools is outmoded, impractical, limited in hands-on training, and lower in standards compared to that in private schools. Therefore, the core objective of this study is to examine the nexus between quality education and occupational skills, drawing insights from educators and individuals. This article reviews a literature review and analysis of data, including surveys, interviews, and focus groups, to develop a framework for describing and discussing quality education in the context of Kathmandu, Nepal. Anchored in the interpretivist paradigm, this study employs an interpretative phenomenological methodology to explore and elucidate the lived experiences of the participants. The findings depict that good teaching, a supportive learning environment, and active involvement in extracurricular activities play a significant role in providing quality education, which helps students become professionally competent.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".