Learning to build institutional capacity through knowledge-based partnerships between universities and industry: lessons for engineering ecosystems from computing in Kenya
Bibliographic record
Abstract
Two of the main challenges facing engineering ecosystems in Africa are 1) enabling universities to produce more high-quality research, and 2) creating more linkages between universities and industry to ensure that research is used, and that highly skilled workers have appropriate knowledge and training. But how can we understand knowledge-focused linkages between universities and industry in relation to other capacities and capacity building efforts within engineering systems? What are the challenges and benefits of building these linkages, and what processes and practices lead to lasting partnerships? We address these questions for the case of computing and information technology in Kenya. Our analysis comes from a three-year project which created and evaluated industrial studentship and fellowship programmes that involved partnerships with companies. University–industry linkages can be understood as an aspect of institutional capacity: a concept that refers to a range of capabilities – important across engineering ecosystems, but especially for universities – that enables production of high-quality and locally relevant research and contributes to the professional development of graduates. Other interrelated aspects of institutional capacity include mechanisms to support acquisition of funding; norms of mentorship, peer support, and scholarly communication; and structures that enable researchers to balance research and teaching. Our data reveal that while some of these capabilities are weak or missing in the Kenyan computing ecosystem, intermediary organisations can act as knowledge brokers to build linkages and facilitate learning between universities and industry. However, these linkages must be built alongside other dimensions of institutional capacity, especially social components like mentorship and peer-to-peer learning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| 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".