Open Innovation and Knowledge Appropriation in Africa Micro and Small Enterprises (MSEs)
Bibliographic record
Abstract
This article seeks enhanced understanding of the dynamics of open innovation and knowledge appropriation in African settings. More specifically, the authors focus on innovation and appropriation dynamics in African micro and small enterprises (MSEs), which are key engines of productivity on the continent. The authors begin by providing an expansion of an emergent conceptual framework for understanding intersections between innovation, openness and knowledge appropriation in African small-enterprise settings. Then, based on this framework, they review evidence generated by five recent case studies looking at knowledge development, sharing and appropriation among groups of small-scale African innovators. The innovators considered in the five studies were found to favour inclusive, collaborative approaches to development of their innovations; to rely on socially-grounded information networks when deploying and sharing their innovations; and to appropriate their innovative knowledge via informal (and, to a lesser extent, semi-formal) appropriation tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".