Introduction to Thematic Issue: Collaborative Innovation in African Settings
Bibliographic record
Abstract
The two overarching questions currently driving Open AIR's research are: How can open, collaborative innovation help businesses scale up and seize the new opportunities of a global knowledge economy? And which knowledge governance policies will best ensure that the social and economic benefits of innovation are shared inclusively? These questions are approached through research work organised into five (often overlapping) thematic orientations: technology hubs, informal innovation, Indigenous entrepreneurs, innovation metrics, and laws and policies. Open AIR's core research methods are situational analysis via case studies; action-based research; and grounded theory-building. The researchers come from a wide range of disciplines, including law, economics, management, political science, and public policy. The six articles in this thematic issue reflect the diversity of the Open AIR network, of its approaches to understanding collaborative innovation in African settings, and of its conceptions of the social, economic, technological and policy dimensions that impact, and are impacted by, innovation. Also reflected in the articles is the geographical range of the network. Two of the articles include detailed reflections on international and African continental realities, and the four articles grounded primarily in African national and sub-national realities draw on data from the continent's North, East, and Southern regions. The articles' authors include researchers from five of Open AIR's institutional hubs: The American University in Cairo, Strathmore University in Nairobi, the University of Johannesburg, the University of Cape Town, and the University of Ottawa. The opening article, by Gwagwa, Kraemer-Mbula, Rizk, Rutenberg and De Beer, explores one of the most pressing matters, in both practical and policy terms, facing African knowledge-based innovators: deployments of artificial intelligence (AI) on the continent. Framing their analysis in terms of socio-economic inclusion, the authors argue that if AI is to be of true benefit to the continent, African policymakers will need to craft enlightened responses to matters of gender empowerment, cultural and linguistic diversity, and shifts in labour markets. 1 https://openair.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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".