Adaptive capacity creation in the Stó:lō Research and Resource Management Centre (Stó:lō Nation, BC) and the Fort Apache Heritage Foundation (White Mountain Apache Tribe, AZ)
Bibliographic record
Abstract
Indigenous peoples are disproportionately threatened by a changing climate. Research indicates that U.S. Tribes and Canadian Aboriginal Peoples are experiencing detrimental climate change effects. In this context, Indigenous organizations deserve special consideration as community-based pathfinders for collective welfare. I engaged with two Indigenous organizations that share cultural heritage stewardship missions—the Stó:lō Research and Resource Management Centre (Stó:lō Nation, BC) and the Fort Apache Heritage Foundation (White Mountain Apache Tribe, AZ)—to investigate perceptions of climate effects and develop recommendations for organizational support of community adaptive capacity. Research methods included engagement with organizational collaborators, semi-structured interviews with organizational representatives and community members, and organizational documents review. Results indicate that community members are experiencing increase in extreme weather events, changes in water quantity and quality, reductions in long-term water and food security, and reduced access to traditional resources and traditional practices. Results identify diverse opportunities to enable adaptation, most of which are case study-specific. Educational services and information dissemination, cultural perpetuation services, and cooperation facilitation comprise organizational services associated with adaptive capacity enhancement in both case studies. I conclude that Indigenous organizations hold significant potential to support communities in adapting to a changing climate. I identify recommendations to boost and actualize this potential.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".