As we have always done: Sharing Māori, Anishinaabe and Gàidheil responses to climate challenge
Bibliographic record
Abstract
Indigenous peoples throughout the world are under considerable cultural and ecological pressure in the face of a rapidly warming world. While contexts and Indigenous knowledge systems are specific, there is much that can be learned from knowledge exchange and collaborations with other Indigenous communities. This article reports on a growing conversation across diverse cultural biospheres (Aotearoa New Zealand, Turtle Island, and Alba/Scotland) regarding inclusive Indigenous-led strategies of multigenerational resilience addressing human-environmental wellbeing. Drawing on indigenist research methodologies, it integrates recent research pertaining to each geo-cultural context, with online international Wisdom Councils collectively participated in by the three regions. Māori systems of healing, Anishinaabe renewable energy-harvesting protocols, and Gàidheil “cultural darning and mending” climate challenge strategies are discussed, including the potential of their cross-context relevance. Attention to non-binary ways of conceptualizing Indigenous identities (human and more than-human), including attention to diverse gender and sexual identities within Indigenous-led climate emergency responsiveness, are also discussed as a critical cross-cutting strategy.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".