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Harnessing Indigenous Entrepreneurship in the 21st Century for Sustainable Development

2025· book-chapter· en· W4407733350 on OpenAlexaboutno aff
Bonny Ngakane

Bibliographic record

VenueAdvances in religious and cultural studies (ARCS) book series · 2025
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEntrepreneurshipGovernment (linguistics)Sustainable developmentTraditional knowledgeProduct (mathematics)BusinessEconomic growthPolitical scienceGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.254
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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