Research Leadership in Africa: Perspectives from the Social Sciences, Humanities, and Arts
Bibliographic record
Abstract
Global and human development and freedoms increasingly thrive on robust and policy-oriented research and related activities. Yet, the African research landscape faces a myriad of challenges, resulting in a very unequal continent in terms of research and research capacity. The prevailing research inequities and challenges in Africa are even more pronounced in the social sciences, humanities, arts, and related fields (SSHA). Here, the strengths and impact of scholarship in SSHA fields are often overshadowed by deficits and apparent preferential investment in research in science, technology, engineering, and mathematics-related fields. In response, the African Academy of Sciences commissioned a study in 2020 to generate evidence on the SSHA research support landscape in Africa. This paper summarizes findings from the literature review, key informant interviews, a bibliometric analysis, a survey with a sample of 670 respondents from SSHA communities in Africa, and a series of focus group discussions. We highlight key messages and make recommendations focussing on lessons learnt opportunities, and priorities for intervention to enhance significant SSHA research leadership capacity strengthening and, ultimately, minimize research inequalities 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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".