The bridge between two worlds: Global South researchers' journeys through Global North academic training and beyond
Bibliographic record
Abstract
International training of Global South researchers represents a strategic investment that yields substantial returns, rather than the traditional “brain drain” framing. This perspective synthesises the experiences of infectious disease epidemiologists from Colombia, Indonesia, Kenya, Nigeria, and Sierra Leone who completed training in Global North institutions between 2015-2024. Despite facing challenges, language barriers, and representational pressures, we demonstrate how Global South researchers transform these obstacles into unique strengths that enhance local research capabilities. Our experiences also show that Global South researchers serve as vital bridges between academic worlds, contributing irreplaceable contextual knowledge while building collaborative networks that advance infectious disease epidemiology research regardless of geographic location. We provide four strategic recommendations for better infectious disease epidemiology research ecosystems: 1) creating supportive institutional environments in Global North institutions, 2) building sustainable partnerships that strengthen home institutions, 3) embracing individual agency and responsibility, and 4) strengthening regional collaborations while adapting to evolving global contexts. Our narrative progresses from challenges to empowerment, demonstrating that Global South researchers are valuable contributors essential to advancing infectious disease epidemiology research. • International mobility of Global South researchers challenges “brain drain” narratives, delivering substantial returns through knowledge transfer and collaboration networks • Our experience shows how Global South researchers pursuing international training transform challenges into unique research strengths • Global South researchers pursuing international training experience dynamic role reversals: contextual experts abroad become technical experts upon return home • International training creates privilege dynamics requiring intentional efforts toward equitable research ecosystems • Four recommendations providing actionable pathways for supportive institutions (both in the Global North and Global South) and sustainable collaborations (especially South-South collaborations)
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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 | 0.025 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.030 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.036 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".