The national entrepreneurship framework conditions in sub-Saharan Africa: a comparative study of GEM data/National Expert Surveys for South Africa, Angola, Mozambique and Madagascar
Bibliographic record
Abstract
Abstract Entrepreneurship is widely argued to be critical for economic development and alleviating extreme poverty. However, entrepreneurship research in sub-Saharan Africa has not received much attention over the last few decades possibly due to a lack of sufficient resources. It is becoming increasingly important as Africa, especially sub-Saharan Africa, is developing rapidly and moving from a resource-based economy to one of innovation and progress. Using data from the Global Entrepreneurship Monitor (GEM), this paper discusses the opinions of national expert informants in Angola, Madagascar, Mozambique and South Africa and looks at the factors which are possibly hindering and inhibiting entrepreneurial development. The results indicate that there are four main inhibitors ranging from lack of access to finance, government policies, regulations and practices for entrepreneurs and the poor levels of entrepreneurship education. Some recommendations are made as to what can be done to assist in promoting economic development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".