The Impacts Of Climate Change On The Health And Well-being Of The Peoples Of Whitefish River First Nation, Ontario
Bibliographic record
Abstract
Indigenous communities in Canada are some of the most vulnerable to the impacts of climate change. Research has tended to focus more on the Arctic region in Canada and more recently, a need to understand how climate change is impacting First Nations communities in the Great Lakes region has emerged. Whitefish River First Nation is an Anishinaabe community located on the northern shores of Georgian Bay in Lake Huron in the Great Lakes Ecosystem of Ontario. This qualitative research study, by working with the Whitefish River First Nation aimed to address how a First Nations community’s health and well-being is being impacted by climate change. Indigenous research methodologies and environmental justice concepts were used to frame this research study. Through focus groups with community members and Elders and key informant interviews, information and knowledge from participants of Whitefish River First Nation was gathered and analyzed. Impacts to the community’s health and well-being as a result of climate change and other environmental stressors was shared. Based on the discussions and adaptation strategies that emerged from the community, this paper outlines recommendations for how to move forward and address the impacts of climate change on First Nations communities in the Great Lakes region.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".