Migration should be a personal choice, not the only one - a reflection on scientific diasporas
Bibliographic record
Abstract
A brain drain phenomenon, i.e., the migration of highly skilled professionals, has represented and still represents a severe loss of intellectual capital for Global South countries. Factors driving this migration include limited research infrastructure, funding constraints, political instability, and the lack of scientific career prospects in the Global South, and the consequences are multifaceted. While this can hinder local development in the Global South, it simultaneously enriches research ecosystems in the Global North, exacerbating existing global inequalities in science and technology. Under this scenario, scientific diasporas represent an effort to counterbalance the brain drain scenario through initiatives that aim to increase science and technology, which are led by self-organized expat professionals and scientists. While we can find some successful examples of international cooperation driven by scientific diasporas, without a proper organization and full participation of the governments of the countries of origin, scientific diasporas can become dysfunctional and can promote more migration upon training. We, five early-career scientists, discuss our perspectives and personal reflections on scientific diasporas. We describe three migration models of highly skilled professionals, starting with a brain drain model, scientific diaspora, and dysfunctional scientific diaspora, and provide some ideas to help the implementation of successful scientific diasporas. We believe that migration must be a personal decision seeking scientific growth and professional development, and not the only option we should have to pursue a fulfilling career in science.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".