Leveraging community agroecological values across scales for food system transformation in Ka’a’gee Tu First Nation, Northwest Territories
Bibliographic record
Abstract
Communities in northern Canada are adopting community gardens as a means to address food insecurity, which has been exacerbated by climate change, rising food costs, and limited access to traditional and nutritious foods. Despite these initiatives, many northern communities lack the essential resources required to sustain such projects. This study seeks to address this gap through a Participatory Action Research approach, whereby community members identify both available resources and those necessary for maintaining their community garden, as well as potential regional and extra-regional opportunities for sustaining food system projects. The Community Agroecological Values Framework (CAVF) is applied to food system planning in Kakisa, Northwest Territories (NWT). The findings indicate that while the community has successfully leveraged regional and extra-regional resources by building relationships with organizations outside the territory, barriers such as unstable relationships and conflicting perspectives regarding land use and agriculture have constrained access to critical regional supports, including gardening knowledge networks, funding, and training opportunities. This study highlights the importance of both short-term regional support and long-term local capacity building to establish foundational knowledge and foster enthusiasm for food production over time. Lessons learned from strategies aimed at building local capacities indicate that both short-term regional assistance and sustained community-level capacity development are crucial for establishing foundational knowledge and enthusiasm for gardening in the North. These findings contribute to the design of a community food system action plan, emphasizing the necessity for collaborative strategies to build well-being and promote the sustainable transformation of food systems in northern Indigenous communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".