Natural Hazards, Climate Change, and Indigenous Knowledge and Stewardship: Moving Toward a Resilience-Based Model
Bibliographic record
Abstract
Abstract The increased frequency and intensity of natural hazards associated with climate change represents a major risk to Indigenous peoples . Yet, the existing vulnerability-based model of natural hazard mitigation and adaptation is reactive and largely treats Indigenous peoples as “vulnerable”—passive actors, whose knowledge and stewardship are typically ignored. To meaningfully mitigate and adapt to the increasing impacts of changing climates, a transformation is required towards a proactive and holistic resilience-based model, grounded in Indigenous knowledge and stewardship. A resilience-based model empowers Indigenous peoples to proactively steward their lands towards health as the primary goal, thus reducing ecosystem vulnerability, and in doing so, community members strengthen their connection to the land and enhance their identity and agency, with documented positive health and well-being outcomes. Transitioning to a resilience-based approach requires structured learning and action-based approaches that treat resilience as both a process and an outcome, and strengthened in a positive cycle. Four interdependent elements are important to catalyze a resilience-based approach: (a) devolving stewardship to Indigenous Peoples , guided by Indigenous knowledge; (b) strengthening localized governance, in ways consistent with local values; (c) adequately resourcing stewardship and governance; and (d) monitoring and evaluating stewardship using “transdisciplinary” approaches that draw from Indigenous and Western knowledge systems in respectful ways, and to guide stewardship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".