MétaCan
Menu
Back to cohort
Record W4381614741 · doi:10.1504/ijesb.2023.131648

Contextual impact on indigenous entrepreneurs around the world: geographic location, socio-cultural context and economic structure

2023· article· en· W4381614741 on OpenAlexfundno aff
Prescott C. Ensign

Bibliographic record

VenueInternational Journal of Entrepreneurship and Small Business · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEntrepreneurshipIndigenousEconomic geographyContext (archaeology)GeographyEconomic growthRegional scienceBusinessEconomics

Abstract

fetched live from OpenAlex

The number of Aboriginal people in the world is greater than that of the USA and almost equal to that of the EU. Yet politically and economically, they are among the weakest. Entrepreneurship is viewed as a means of empowerment and wealth creation for Indigenous individuals and communities. This paper explores the impact that geographic embeddedness, indigenous cultural factors, and mainstream economic structures have to help or hinder starting and operating an Aboriginal business. A conceptual framework of these contextual factors was constructed as an analytical tool for a qualitative deductive examination of these dynamics in cases, studies, and reports of over 50 remote, rural and urban instances of Indigenous entrepreneurship in 12 countries. Findings strongly point to the interconnectedness of these contextual factors, which provide opportunities for greater leveraging of enterprise creation and development. A Western-Eurocentric perspective and focus on the dominant culture's business model cause the underutilisation of Aboriginal ways.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0030.001
Open science0.0000.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Entrepreneurship and Small BusinessSame topicEntrepreneurship Studies and InfluencesFrench-language works237,207