Mining versus Indigenous Protected and Conserved Areas: Traditional Land Uses of the Anisininew in the Red Sucker Lake First Nation, Manitoba, Canada
Bibliographic record
Abstract
Indigenous traditional land uses, including hunting, fishing, sacred activities, and land-based education at the Red Sucker Lake First Nation (RSLFN) in Manitoba, Canada, are impacted by mining. The Red Sucker Lake First Nation (RSLFN) people want their territories’ land and water to be protected for traditional uses, culture, and ecological integrity. Towards this goal, their Island Lake Tribal Council sought support for an Indigenous-protected and conserved area (IPCA) in their territory, outside of existing mining claims, but without success. The two-eyed seeing approach was adopted in this study. Traditional land use mapping and interviews were undertaken with 21 Indigenous people from the RSLFN, showing that many traditional land uses are concentrated on greenstone belts. The interviews revealed that mining exploration has resulted in large petroleum spills, noise distress, private property destruction, wildlife die-offs, and animal population declines. These issues negatively impact RSLFN’s traditional land use practices, ecosystem integrity, and community health. Governments need to partner with Indigenous communities to reach their biodiversity targets, particularly considering northern Canada’s peatlands, including those in the RSLFN territory, surpassing Amazon forests for carbon storage. The role of critical minerals in renewable energy and geopolitics has colonial governments undermining Indigenous rights, climate stabilization, and biodiversity to prioritize extractivism. Mining at the RSLFN has environmental impacts from exploration to decommissioning and after, as well as the massive infrastructure required that includes roads, hydro, and massive energy supplies, with a proposed multimedia national Northern Corridor to export RSLFN’s resources and other resources to six ports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".