Transition to the Fourth Industrial Revolution: Africa’s Science, Technology and Innovation Framework and Indigenous Knowledge Systems
Bibliographic record
Abstract
Abstract Despite elaborate efforts at Science Technology and Innovation (STI) policy enunciation, Africa has yet to optimally engage with how best to locate and position Indigenous or traditional knowledge (IK/TK) and its stakeholders in the new and emergent technological dynamics often designated as the fourth industrial revolution (4IR) and its bioeconomy components. Given the disconnect over IK/TK systems in African STI policy instruments, the paper argues for a deliberate Indigenous knowledge sensitive continental STI strategy without excluding integral opportunities in other realms such as intellectual property. Such approach to STI is necessary to ensure that Africa is well positioned to leverage and optimise its factor endowments in Indigenous knowledge and underlying systems for its production. Indigenous knowledge is crucial for continental Africa’s participation and ability to benefit from all facets of knowledge production under the 4IR innovation ecosystem. The significance of Indigenous knowledge and its ramification for STI in Africa continues to resonate in the context of the push for equitable access to the benefits of science, technology and innovation especially taking into account the bioeconomy adjunct of the 4IR.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".