The characteristics of highly cited researchers in Africa
Bibliographic record
Abstract
Very little is known about the characteristics of highly cited scientists in Africa. This is unfortunate as highly cited researchers are seen as key drivers of knowledge production for their countries and as important conduits of frontier knowledge into the local academic research community and society in general. In this article, we combined bibliometric and survey data to identify which researchers are producing highly cited research in Africa, and we employed econometric analysis to understand which characteristics are associated with the likelihood of being highly cited. Overall, our results suggest that, on average, researchers who produce more scientific publications in a year, collaborate more often with non-African partners, and do their highest qualification in an Anglo-Saxon university (the USA, the UK, Canada, or Australia), have a higher probability of producing highly cited research. We conclude by arguing about the duality of our results. On the one hand, collaborating with frontier universities seems to be a crucial mechanism that allows researchers to develop scientific capabilities. On the other hand, policy makers should be aware that research assessment in African countries should go beyond measuring scientific impact in the academic community. Otherwise, incentives will be in place to stimulate winners that are already well connected with the global scientific elite.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".