Reclaiming Land, Identity and Mental Wellness in Biigtigong Nishnaabeg Territory
Bibliographic record
Abstract
Indigenous peoples globally are pursuing diverse strategies to foster mental, emotional, and spiritual wellness by reclaiming and restoring their relationships to land. For Anishinaabe communities, the land is the source of local knowledge systems that sustain identities and foster mino-bimaadiziwin, that is, living in a good and healthy way. In July 2019, the community of Biigtigong Nishnaabeg in Ontario, Canada hosted a week-long land camp to reclaim Mountain Lake and reconnect Elders, youth and band staff to the land, history, and relationships of this place. Framed theoretically by environmental repossession, we explore the perceptions of 15 participating community members and examine local and intergenerational meanings of the camp for mental wellness. The findings show that the Mountain Lake camp strengthened social relationships, supported the sharing and practice of Anishinaabe knowledge, and fostered community pride in ways that reinforced the community's Anishinaabe identity. By exploring the links between land reclamation, identity, and community empowerment, we suggest environmental repossession as a useful concept for understanding how land reconnection and self-determination can support Indigenous mental wellness.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".