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Record W2604694661 · doi:10.1177/1177180117700816

Whakatipu rawa ma ngā uri whakatipu: optimising the “Māori” in Māori economic development

2017· article· en· W2604694661 on OpenAlexaff
Shaun Awatere, Jason Paul Mika, Māui Hudson, Craig Pauling, Simon J. Lambert, John D. Reid

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousEntrepreneurshipIdeologyExternalityProfit (economics)Context (archaeology)SociologyEconomic growthBusinessPublic relationsPolitical scienceEconomicsFinanceGeographyMicroeconomicsEcologyBiology

Abstract

fetched live from OpenAlex

One of the great challenges for indigenous and non-indigenous entrepreneurs in the twenty-first century is to move beyond profit maximisation as an acceptable modality for doing business and gravitate towards the concept of socially optimal outcomes, where maximising community well-being and minimising externalities to the natural environment and social justice are paramount. We present findings from a case-study analysis of Māori enterprises that demonstrate a wealth of successfully kaupapa Māori (Māori ideology)-attuned strategy and policy. The case studies provide practical examples of the incorporation and expression of kaupapa Māori values into strategy and policy of Māori enterprises. We also identify the numerous challenges to implementing kaupapa Māori in the management of Māori Asset Holding Institutions and offer a way forward. Although the case studies are context specific, they provide some key principles and learning that can guide the greater uptake of kaupapa Māori entrepreneurship.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0010.002
Open science0.0030.000
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 head0.032
GPT teacher head0.357
Teacher spread0.325 · 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 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

Citations29
Published2017
Admission routes1
Has abstractyes

Explore more

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