The Yetwánaý Project: A Skwxwú7mesh Case Study for Land and Plant-based Health and Wellness
Bibliographic record
Abstract
The Yetwánaý Project is a community-led, land-based case study from the Skwxwú7mesh (Squamish) Nation that explores how reconnection with culturally important plants can support Indigenous health and wellness, particularly in relation to the prevention and management of Type 2 Diabetes (T2D). Rooted in Community-Based Participatory Action Research (CBPAR) and Insurgent Research methodologies, the project engaged over 200 participants through seasonal community gatherings, land-based harvesting sessions, and interactive activities grounded in Skwxwú7mesh culture. Data sources included harvest surveys, community feedback, planning committee meeting notes, and facilitation materials. A thematic analysis was employed to identify key themes emerging from this diverse dataset. The analysis revealed six interrelated themes, including the role of Indigenous plants in wellness, ancestral continuity, cultural implications of T2D, colonial barriers to land access, and the importance of Indigenous researchers. The Yetwánaý Project is an example of what respectful research with Indigenous community partners can look like and demonstrates the value of participatory, decolonial, and culturally grounded approaches to ethnobotanical research. The paper concludes with “wise practices” to guide ethical and reciprocal research partnerships in Indigenous health contexts.
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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 | 0.003 | 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 | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".