Reclaiming Mountain Lake: Applying environmental repossession in Biigtigong Nishnaabeg territory, Canada
Bibliographic record
Abstract
The concept of environmental repossession responds to a global movement led by Indigenous peoples to reclaim their territories and ways of life. As Indigenous wellness is intimately tied to relationships to land, processes of environmental repossession are a means to revitalize knowledge systems, identities and relationships that foster strong and healthy communities. Due to historic and ongoing forces of dispossession, the Anishinaabe community of Biigtigong Nishnaabeg has experienced limited access to Mountain Lake, a culturally and historically significant place in their ancestral territory. In the summer of 2018, the Chief and Council of Biigtigong constructed two cabins along the shores of Mountain Lake for community use and, one year later, hosted a week-long camp to bring Elders, youth and band staff together in this place. Drawing from 15 in-depth interviews with participating community members, this study documented the planning and implementation of the cabins and camp at Mountain Lake and examined the community meanings of this process. The findings suggest that the cabins and camp functioned as a local process of environmental repossession through multiple and interconnected steps to reclaim access to Mountain Lake, reintroduce the community to this place and begin remaking community relationships to this land. As Indigenous communities globally seek to reclaim their territories and rights to land, this article speaks to the tensions of this work and the structures that support its practice locally.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".