Métis Knowledges and Climate Change: Towards Adaptation in Southeastern Manitoba Michif Communities
Bibliographic record
Abstract
Climate change poses a global existential threat, with Indigenous knowledges gaining momentous recognition for their critical role in addressing this challenge. Manitoba, experiencing rapid warming, faces ecological, social, and economic challenges, particularly negatively impacting Indigenous communities. This research, guided by my epistemological position as a Red River Métis woman, explores the contribution of Métis knowledges for climate change adaptation in the Homeland of the Red River Métis. The main objective of this thesis is to characterize Métis knowledges in Michif communities in Southeastern Manitoba to understand the linkages between colonization, land use, and climate change risks to develop adaptation approaches based on Métis knowledges. Crucially, this thesis will also aid in advocating for the inclusion of Métis ways of knowing in climate change adaptation policies. Grounded in relationships, experiential knowledge, and kinship, this research follows Indigenous Research Methodologies, fostering respect, relevance, responsibility, and reciprocity. This research is also based on Farrell Racette's Métis Kitchen Table Theory (Farrell Racette, 2004) a community-centred, anti-patriarchal, and anti-colonial approach that aligns with a more holistic approach to information exchange based on Métis culture and traditions. This research has the potential to foster effective climate adaptation planning and policy recommendations for Indigenous communities, with a specific focus on Métis knowledges and Métis communities. It addresses the challenges and opportunities faced by these communities to respond to climate change while fore fronting reconciliation and returning sovereignty to these communities. By bringing Métis knowledges into the climate change adaptation policy discourse, this research aims to increase Métis-led cultural and environmental security and sustainability in so-called Canada, initiating a crucial dialogue around including Métis knowledges at policy tables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".