Motivating Factors for International Migration: A Case Study of Nepalese Students Living Overseas
Bibliographic record
Abstract
Nepal has one of the highest rates of student outflow abroad in search of better education and employment opportunities.However, this emerging phenomenon also causes much concern about the so-called 'brain drain' and its implications in the country.This study explores the motivating factor for the out-migration of Nepalese students to other countries.I selected six participants living in different foreign countries through purposive sampling and conducted in-depth telephone interviews to elicit their personal experiences.The results indicate that students are motivated not only by push factors such as scarcity of opportunities in Nepal and political disturbances and by an obsolete education system in the country but also by pull factors such as advanced education systems, more predictable employment, and improved quality of life abroad.This study concludes that while migrating abroad gives students opportunities to grow personally and professionally, it also contributes to Nepal's loss of skilled individuals.Therefore, this study suggests that improving domestic education, providing long-term job opportunities, and promoting good governance are crucial for Nepal to retain its human capital and encourage students to return home after studying abroad.
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.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".