Rebuilding a KINShip Approach to the Climate Crisis: A Comparison of Indigenous Knowledges Policy in Canada and the United States
Bibliographic record
Abstract
Indigenous Peoples have developed Indigenous knowledge systems that have been fundamental to stewarding their territories for millennia. Yet, there remains a continued need for more recognition and frameworks that can equitably promote Indigenous knowledges and their vital role in addressing the ongoing climate crisis. Given the evolving policy landscape for Indigenous Peoples in relation to their Indigenous knowledges, it is important to monitor and reflect on how these policies may impact Indigenous communities. To support further policy discourse, we therefore carried out a policy study to compare Indigenous knowledge policy and frameworks in Canada and the United States including their similarities, differences, and gap areas. We more specifically aimed to formally analyze key Indigenous knowledges policy in both countries to provide further reflection on the Canadian Indigenous knowledges policy landscape while also proposing key policy recommendations. Findings from our policy review demonstrate that Indigenous knowledges policy in both countries is still fairly new with a lack of clarity on the success of operationalizing these policies across jurisdictions and regions. Furthermore, the current states of policies and frameworks exemplify the continued need to acknowledge the contribution of Indigenous knowledges from a rights-based perspective alongside Western science in addressing climate change, including how it impacts Indigenous Peoples.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".