Research excellence in Africa: Policies, perceptions, and performance
Bibliographic record
Abstract
Our article discusses various features of research excellence (RE) in Africa, framed within the context of African science granting councils (SCGs) and pan-African RE initiatives. Our survey, collecting responses from 106 researchers and research coordinators across Africa, highlights the diversity of opinions and preferences with regards to Africa-relevant dimensions of RE and related performance indicators. The results of the survey confirm that RE is a highly multidimensional concept. Our analysis shows how some of those dimensions can be operationalised into quantifiable indicators that may suit evidence-based policy discourses on research quality in Africa, as well as research performance assessments by African SCGs. Our indicator case study, dealing with the top 1 per cent most highly cited research publications, identifies several niches of international-level RE in the African continent while highlighting the role of scientific cooperation as a driving force. To gain a deeper understanding of RE in Africa, it is important to take into account the practical challenges faced by researchers and research funding agencies to align and reconcile socioeconomic interests with international notions of excellence and associated research performance indicators. African RE should be customised and contextualised in order to be responsive to African needs and circumstances.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".