Ka Mua, Ka Muri—Walking Backwards into the Future: Revitalizing Indigenous Economies and Economies of Well-Being
Bibliographic record
Abstract
The concept of and desire for well-being economies are rising in prevalence as traditional business paradigms are questioned and alternative framings are being sought. Indigenous peoples, their economies, and their approach to business can provide a rich source of learning to enable and help facilitate a transition to economies of well-being. As Indigenous peoples are emerging from their colonial pasts, they are becoming more empowered to make investment choices, use business models, and form partnerships grounded in their worldviews, which are often well aligned with a well-being economy. In this paper, we note some of the obstacles Indigenous economies have faced and outline success stories where Indigenous tribes/communities/peoples have created business opportunities that are underpinned by their worldviews and are thriving commercially. We then describe a conceptual framework for how Indigenous peoples could support a broader transition to economies of well-being. Indigenous worldviews can provide a way for ‘reimagining’ the economy. Growing the self-determination of Indigenous peoples provides greater opportunities to create ‘reimagined business models’ that align with a reimagined economy and Indigenous worldviews, and thus helps demonstrate ways to start a transition toward economies of well-being. The findings, insights, and conclusions outlined in this paper were drawn from a convened workshop and subsequent dialogue of 24 Indigenous and non-Indigenous scholars from Australia, Canada, New Zealand, and the United States of America.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".