Bibliographic record
Abstract
The discourse on indigenous knowledge has incited a debate of epic proportions across the world over the years. In Africa, especially in the sub-Saharan region, while the so-called indigenous communities have always found value in their own local forms of knowledge, the colonial administration and its associates viewed indigenous knowledge as unscientific, illogical, anti-development, and/or ungodly. The status and importance of indigenous knowledge has changed in the wake of the landmark 1997 Global Knowledge Conference in Toronto, which emphasised the urgent need to learn, preserve, and exchange indigenous knowledge. Yet, even with this burgeoning interest and surging call, little has been done, especially in sub-Saharan Africa, to guarantee the maximum exploitation of indigenous knowledge for the common good. In view of this realisation, this paper discusses how indigenous knowledge can and should both act as a tool for promoting the teaching/learning process in Africa's public education and address the inexorably enigmatic amalgam of complex problems and cataclysms haunting the world.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".