Decolonial perspectives on climate change: Learning from the Kainai First Nation in Canada
Bibliographic record
Abstract
This study focuses on the reflections and insights of Indigenous Elders from the Kainai First Nation in Canada regarding climate change challenges and potential solutions. Through a decolonial and Elder-led land-based learning process, the research team captured the traditional land-based knowledge of the Elders, rooted in their profound understanding of the interconnectedness between humans, nature, and climate. The findings showcase the shared concerns of Indigenous Elders and emphasize the imperative of recognizing and valuing Indigenous knowledge systems as crucial resources for climate adaptation and mitigation strategies. Indigenous land-based knowledge offers a holistic perspective that encompasses social, cultural, and spiritual dimensions, advocating for sustainable practices and harmonious coexistence with the environment. This decolonial study identifies specific strategies and practices proposed by Indigenous Elders as potential solutions to climate change challenges. The insights shared by Indigenous Elders emphasize the urgency of integrating Indigenous knowledge systems into global efforts to address climate change. By honoring and learning from their wisdom, societies can cultivate a more holistic and sustainable approach to climate adaptation and mitigation, fostering resilience, biodiversity conservation, and the well-being of both human and non-human communities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".