Stimulating Academic Entrepreneurship through Technology Business Incubation: Lessons for the Incoming Sponsoring University
Bibliographic record
Abstract
Universities facilitate academic entrepreneurship or their ‘third mission’ by making available supporting mechanisms such as science and technology parks, incubators, and entrepreneurship programs. Botswana’s STEM University seeks to develop a technology park in which it will commercialize the research and intellectual property developed by its faculty members, students, research centers and the country’s private sector through incubation and other processes. As a business support process, technology business incubation nurtures start-up companies and mitigates the risk of their early failure. In this enabling environment, start-ups can concentrate on technology transfer and later “hatch” or leave the incubator financially viable and self-sustaining. Pursuing academic entrepreneurship and the university-model of technology business incubation present benefits for the country, the local community and the university in terms of economic development, economic diversification, job creation, technology development, viable firms, successful products, and the enhancement of university income and prestige. However, university and faculty culture, and the extent of faculty members’ knowledge and skills in entrepreneurship and social capital may temper this potential. Utilizing a narrative review of the literature, this paper sought to identify critical issues a newly-participating university should be aware of as it seeks to adopt the university-model of business incubation to facilitate its transformation from a primary focus on its traditional research and teaching missions to one also based on the formal commercialization activities characterizing academic entrepreneurship. The paper informs on approaches the university may adopt to encourage academic entrepreneurship among its faculty members.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".