Entrepreneurial Education in a Tertiary Context: A Perspective of the University of South Africa
Bibliographic record
Abstract
South Africa is characterised by high unemployment levels, a low Total Early Stage Entrepreneurial Activity rate, and a high small business failure rate. Entrepreneurship and small business development is seen as a solution to end unemployment in South Africa. A study to understand how to improve small business support was conducted at the University of South Africa and has shown that alumni are unable to apply theoretical knowledge acquired from their studies. The purpose of this article is to explore the potential of the University of South Africa in becoming more entrepreneurial to address the aforementioned challenges. A desk study that reviewed literature was conducted to identify different constructs associated with an entrepreneurial university, namely entrepreneurial education, research and development, innovation, commercialisation and incubation, and stakeholders. In addition to traditional teaching methods, various alternative approaches can be used to stimulate entrepreneurial education to develop the skills of learners/students. To address these challenges a closer relationship between academia, government, and industry is paramount. It is recommended that universities incorporate entrepreneurial education in all their qualifications, expose students to on-the-job training, assist with the incubation of business ideas that students have, and provide a platform for cross-pollination of knowledge between industry, academia, and government.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".