WAMPUM Adaptation framework: eastern coastal Tribal Nations and sea level rise impacts on water security
Bibliographic record
Abstract
Sea level rise (SLR) poses significant threats to northeast and mid-Atlantic Tribal Nations' climate and water justice. Existing SLR adaptation frameworks do not include Indigenous knowledge. Furthermore, SLR adaptation policy prioritizes economic and property rights and is misaligned with Indigenous coastal protection priorities. If Tribal Nations are to respond effectively to SLR then adaptation frameworks must be designed and developed by Indigenous Peoples for Indigenous Peoples. Eastern coastal Tribal Nations have a unique history of survival and resilience despite settler-colonial expansion in the northeast and mid-Atlantic regions of what is currently referred to as the United States. Experiences of eastern Atlantic coastal Tribal Nations highlight innovative response strategies for SLR adaptation and coastal stewardship practices not reflected in existing adaptation frameworks for the region. Indigenist SLR adaptation frameworks that utilize Indigenous knowledge are needed to combat water security issues resulting from SLR risks such as flooding, saltwater intrusion, storm surge, and erosion. This article proposes the WAMPUM adaptation framework informed by northeastern and mid-Atlantic coastal Tribal Nation science and knowledge systems for climate change adaptation to SLR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".