Harnessing Indigenous Entrepreneurship in the 21st Century for Sustainable Development
Bibliographic record
Abstract
This chapter explores the harnessing of indigenous entrepreneurship (IE) in Botswana for sustainable development in the 21st century. It examines two case studies of Indigenous entrepreneurs in Botswana, highlighting the use of traditional and modern knowledge. One of these Indigenous entrepreneurs sells donkey milk products, and another deals with fashion apparels. Data were collected through in-depth interviews with participants from two indigenous ventures in Botswana. Data were also gathered from published secondary sources with information on Indigenous entrepreneurship in Botswana and other countries like Canada, Nigeria, South Africa and Peru. The analysis revealed a lack of dedicated structures and policies for IE, with government support being generic. Indigenous Knowledge Systems (IKS) are identified as valuable capital for start-ups, aiding adaptation and innovation. Indigenous entrepreneurs primarily use IKS for product development and digital technologies for marketing. The chapter recommends enacting and implementing specific policies and structures to govern IE. It suggests the Ngakane model as a guide for policymakers to sustain IE through modern technology integration. The model also addresses challenges faced by Indigenous entrepreneurs, aiming to foster a sustainable and innovative Indigenous entrepreneurial ecosystem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".