Vulnerability to Brain-Drain among Academics in Institutions of Higher Learning in Ethiopia
Bibliographic record
Abstract
This study investigated the extent, causes, and correlates of vulnerability to brain-drain among Ethiopian academics in higher education institutions (HEIs). The sample constituted a total of 103 faculty members (Females 9.3% and Males 90.7%) drawn from three colleges and four faculties affiliated to the Debub University. Data were collected through self-reported measures assessing vulnerability to brain-drain (external brain-drain conceptualized as intention to remain in a western country given that they would have opportunities for further study or research; and internal brain drain defined as a brain circulation within the country), affective job characteristics (job satisfaction and organizational commitment), and work environment factors. The results show that affective job characteristics and work environment variables significantly predicted vulnerability to internal brain-drain. While external brain drain is associated with vulnerability to internal brain drain and organizational citizenship behavior (OCB). A closer investigation into the prominence of the pull and push factors further disclosed that working condition and the salary are the outstanding ones. Implications of the findings for policy making are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".