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Does Entrepreneurship Meet the Aspirations of Canada’s Aboriginal Peoples?

2018· article· en· W2817009652 on OpenAlexaffabout
Albert E. James, Christopher M. Hartt, Ashley MacDonald, Julie Marcoux, Shelley Price

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIndigenousEntrepreneurshipMetisField (mathematics)SociologyWork (physics)Political scienceLawEcologyEngineering

Abstract

fetched live from OpenAlex

This paper interrogates the notion of Indigenous Entrepreneurship through an analysis of the literature claiming the concept. [Indigenous is a term used in Canada to refer to Aboriginal, Native, Metis, Inuit, First Nations and others who self-identify]. Five findings are described in the paper, which seem to demonstrate both the uneasy relationship between Indigeneity and academia and the tensions between western ideas of entrepreneurship and values held by many Indigenous communities. Although, entrepreneurship is widely accepted as a tool for economic development (including among Indigenous leaders) the theories of western entrepreneurship likely need significant modification to reconcile with Indigenous culture. Studies found in the research often described cases where entrepreneurship was used as a tool for development and included references to culture and practice, but discussion of the interface between the two appeared to be lacking. Problematic for the field of Indigenous Entrepreneurship is that the academic work found in the field was often in “B” or lower category publications indicating difficulty in reconciling Indigenous Worldviews with accepted academic principles.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0200.006
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.017
GPT teacher head0.245
Teacher spread0.227 · 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 designNot applicable
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

Citations1
Published2018
Admission routes2
Has abstractyes

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