Supporting Indigenous adaptation in a changing climate
Bibliographic record
Abstract
Indigenous peoples are both disproportionately threatened by global climate change and uniquely positioned to enhance local adaptive capacities. We identify actions that support Indigenous adaptation based on organizational and community perspectives. Our data come from two Indigenous organizations that share cultural heritage stewardship missions—the Stó:lō Research and Resource Management Centre (Stó:lō Nation, British Columbia) and the Fort Apache Heritage Foundation (White Mountain Apache Tribe, Arizona). These organizations collaborated with us in exploring community perceptions of climate effects, investigating community adaptation opportunities and constraints, and identifying actions that support Indigenous adaptation. Research methods included engagement with organizational collaborators and semi-structured interviews with organizational representatives and community members and staff. Results confirm that Stó:lō and Apache territories and communities have experienced climate change impacts, such as changes in temperature, hydrology, and increase in extreme weather events. Climate effects are cumulative to colonial depletion of traditional environments and further reduce access to traditional resources, practices, and food security. Results indicated that certain actions are identified by community members as adaptation enablers across case studies—most prominently, perpetuation of Indigenous culture and knowledge, climate education that is tailored to local contexts, collaborative decision-making among community institutions, and integration of climate adaptation into ongoing organizational programs. We conclude that Indigenous-owned organizations are engaged in the expansion of adaptive capacity and hold potential to further support their 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".