Indigenous community health and climate change: Integrating social and natural science indicators
Bibliographic record
Abstract
This presentation describes a pilot study completed in 2013 that evaluated the sensitivity of Indigenous community health to climate change impacts on shorelines in the Salish Sea (Washington State, United States and British Columbia, Canada). Current climate change assessments do not reflect key community health concerns, yet meaningfully including these concerns is vital to successful adaptation plans, particularly for Indigenous communities. Descriptive scaling techniques were employed in facilitated workshops with two Indigenous communities to test the efficacy of ranking six key indicators of community health (Community Connection, Natural Resources Security, Cultural Use, Education, Self Determination and Well-being) in relation to projected changes in the biophysical environment (sea level rise, storm surge, beach armoring) and resultant impacts to shellfish habitat and shoreline archaeological sites. Findings demonstrate that: when shellfish habitat and archaeological resources are impacted, so too is Indigenous community health; not all community health indicators are equally impacted; and, the community health indicators of highest concern are not necessarily the same indicators most likely to be impacted. Based on the findings and feedback from community participants, the exploratory trials were successful, and such a tool may be useful to Indigenous communities who are assessing climate change sensitivities and creating adaptation plans.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".