Promoting community health and climate justice co-benefits: insights from a rural and remote island climate planning process
Bibliographic record
Abstract
Climate change is an environmental crisis, a health crisis, a socio-political and an economic crisis that illuminates the ways in which our human-environment relationships are arriving at crucial tipping points. Through these relational axes, social structures, and institutional practices, patterns of inequity are produced, wherein climate change disproportionately impacts several priority populations, including rural and remote communities. To make evidence-based change, it is important that engagements with climate change are informed by data that convey the nuance of various living realities and forms of knowledge; decisions are rooted in the social, structural, and ecological determinants of health; and an intersectional lens informs the research to action cycle. Our team applied theory- and equity-driven conceptualizations of data to our work with the community on Cortes Island-a remote island in the northern end of the Salish Sea in British Columbia, Canada-to aid their climate change adaptation and mitigation planning. This work was completed in five iterative stages which were informed by community-identified needs and preferences, including: An environmental scan, informal scoping interviews, attending a community forum, a scoping review, and co-development of questions for a community survey to guide the development of the Island's climate change adaptation and mitigation plan. Through this community-led collaboration we learned about the importance of ground truthing data inaccuracies and quantitative data gaps through community consultation; shifting planning focus from deficit to strengths- and asset-based engagement; responding to the needs of the community when working collaboratively across academic and community contexts; and, foregrounding the importance of, and relationship to, place when doing community engagement work. This suite of practices illuminates the integrative solution-oriented thinking needed to address complex and intersecting issues of climate change and community health.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| 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".