Indigenous Land-Based Approaches to Well-Being: The Niska (Goose) Harvesting Program in Subarctic Ontario, Canada
Bibliographic record
Abstract
Historically, goose harvesting provided a source of culturally significant, safe, and nutritious food for the Omushkego Cree of subarctic Ontario, Canada. Disruptions stemming from colonization and climate change have led to a decrease in harvesting, resulting in higher rates of food insecurity. The aim of the Niska program was to reconnect Elders and youth to revitalize goose harvesting activities and associated Indigenous knowledge within the community. The program and evaluation were built using a two-eyed seeing (Etuaptmumk) and community-based participatory research approach. Salivary cortisol, a biomedical measure of stress, was collected before (n = 13) and after (n = 13) participation in the spring harvest. Likewise, cortisol samples were collected before (n = 12) and after (n = 12) the summer harvest. Photovoice and semi-directed interviews were employed after the spring (n = 13) and summer (n = 12) harvests to identify key elements of well-being from an Indigenous perspective. The changes observed in cortisol levels for the spring (p = 0.782) and summer (p = 0.395) harvests were not statistically significant. However, there was a noteworthy increase in the subjective well-being observed through the qualitative measures (semi-directed interviews and photovoice), highlighting the importance of using multiple perspectives when assessing well-being, especially in Indigenous peoples. Future programs should incorporate multiple perspectives when addressing complex environmental and health issues, such as food security and environmental conservation, especially in Indigenous homelands worldwide.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".