The Next Generation of Scientists in Africa
Bibliographic record
Abstract
Young scientists are a powerful resource for change and sustainable development, as they drive innovation and knowledge creation. However, comparable findings on young scientists in various countries, especially in Africa and developing regions, are generally sparse. Therefore, empirical knowledge on the state of early-career scientists is critical in order to address current challenges faced by those scientists in Africa. This book reports on the main findings of a three-and-a-half-year international project in order to assist its readers in better understanding the African research system in general, and more specifically its young scientists. The first part of the book provides background on the state of science in Africa, and bibliometric findings concerning Africa's scientific production and networks, for the period 2005 to 2015. The second part of the book combines the findings of a large-scale, quantitative survey and more than 200 qualitative interviews to provide a detailed profile of young scientists and the barriers they face in terms of five aspects of their careers: research output; funding; mobility; collaboration; and mentoring. In each case, field and gender differences are also taken into account. The last part of the book comprises conclusions and recommendations to relevant policy- and decision-makers on desirable changes to current research systems in Africa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".