Education—Migration Nexus: Understanding Youth Migration in Southern Ethiopia
Bibliographic record
Abstract
The purpose of this study is to unravel the education–migration nexus in the African context, specifically Ethiopia. It examines why young people terminate their education to migrate out of the country. The study applies de Haas’ aspiration—capability framework and Turner’s macro, meso and micro sociology as its analytical lenses. It offers unique insight into the terrain of youth migration in southern Ethiopia based on empirical data obtained from two rural sub-districts known for high levels of youth out-migration. Data are generated based on interviews with would-be migrant youth, parents, teachers and school principals. The findings reveal that education has both direct and indirect impacts on youth migration. On the other hand, the results indicate that though terminating school could have negative ramifications on human capital accumulation at micro and macro levels, migration can positively impact households and local communities through investments made by individual migrants, migrant-returnees, and remittance-receiving households in small businesses or community development projects, which included better resourced schools.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".