Whakatipu rawa ma ngā uri whakatipu: optimising the “Māori” in Māori economic development
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".