The joy of sharing knowledge: But what if there is no knowledge to share? A critical reflection on human capacity building in Africa
Bibliographic record
Abstract
This article focuses on the current trends and initiatives in human capacity building in Africa. It takes as it starting point that human capacity development is essential for Africa to become an information and know-ledge society and therefore an equal partner in the global sharing of knowledge. Four knowledge areas are identified and discussed. These are education, research and development, brain drain and information and documentation drain. The paper concludes that there is a clear understanding in Africa that its future lies with education and that most African leaders have a strong political will to invest in human capacity building on the continent. It is also clear that much has been done, particularly primary education. Africa will most defi-nitely benefit from this in the long run. Problem areas remain however. These are in the needed growth of research and development and how to address the brain and information drain phenomena.
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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 | 0.062 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.123 |
| Scholarly communication | 0.024 | 0.050 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".