Understanding the key processes of excellence as a prerequisite to establishing academic centres of excellence in Africa
Bibliographic record
Abstract
BACKGROUND: Africa's economic transformation relies on a radical transformation of its higher education institutions. The establishment of regional higher education Centres of Excellence (CoE) across Africa through a World Bank support aims to stimulate the needed transformation in education and research. However, excellence is a vague, and often indiscriminately used concept in academic circles. More importantly, the manner in which aspiring institutions can achieve academic excellence is described inadequately. The main objective of this paper is to describe the core processes of excellence as a prerequisite to establishing academic CoE in Africa. METHODS: The paper relies on our collaborative discussions and real-world insight into the pursuit of academic excellence, a narrative review using Pubmed search for a contextual understanding of CoEs in Africa supplemented by a Google search for definitions of CoEs in academic contexts. RESULTS: We identified three key, synergistic processes of excellence central to institutionalizing academic CoEs: participatory leadership, knowledge management, and inter-disciplinary collaboration. (1) Participatory leadership encourages innovations to originate from the different parts of the organization, and facilitates ownership as well as a culture of excellence. (2) Centers of Excellence are future-oriented in that they are constantly seeking to achieve best practices, informed by the most up-to-date and cutting-edge research and information available. As such, the process by which centres facilitate the flow of knowledge within and outside the organization, or knowledge management, is critical to their success. (3) Such centres also rely on expertise from different disciplines and 'engaged' scholarship. This multidisciplinarity leads to improved research productivity and enhances the production of problem-solving innovations. CONCLUSION: Participatory leadership, knowledge management, and inter-disciplinary collaborations are prerequisites to establishing academic CoEs in Africa. Future studies need to extend our findings to understand the processes key to productivity, competitiveness, institutionalization, and sustainability of academic CoEs 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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".