“Land is everything, land is us”: Exploring the connections between climate change, land, and health in Fort William First Nation
Bibliographic record
Abstract
The myriad and cumulative impacts of climate change on land, communities, and health are increasingly evident worldwide. Throughout 2019 and 2020, Fort William First Nation and researchers at Lakehead University conducted a project to document and understand connections among climate change, land, and health from the perspective of Elders, knowledge keepers, and community members with close ties to the land. Fort William First Nation is an Anishinaabe community located on the shores of Lake Superior/Kitchigami in Northern Ontario and within Robinson-Superior Treaty territory. This paper describes the analysis of interviews with 22 community members. Two-Eyed Seeing, an approach that centers the working together of Indigenous and non-Indigenous knowledges and peoples and demands respectful relationship building, guided our project and analysis. Our analysis identified five themes: 1) observations and experiences of changes on the land, 2) lack of care and respect for Mother Earth as the root cause of climate change, 3) healthy land, healthy people, 4) youth and future generations, and 5) (re-)connecting with land and culture. 'All our Relations' is a thread woven across these themes emphasizing that climate change is understood and experienced through relationships and relationality. As concluding thoughts and reflections, we share three specific offerings of particular relevance to those engaging in climate change and health research and action with Indigenous peoples and communities. First, this research has highlighted the importance of explicitly centering land when exploring the links between climate change and health. Second, we reflect on the value of both the Two-Eyed Seeing approach and the Medicine Wheel in guiding climate change and health research. Third, we argue for an explicit focus on relationships and relationality in efforts aimed at understanding and addressing climate change.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".