Healthy people, healthy land: driving sustainable food systems transformation with community agroecological values and Indigenous food systems planning in Kakisa, Northwest Territories, Canada
Bibliographic record
Abstract
Food systems in northern Canada are under severe pressure brought on by climate change, colonial policies, resource extraction, settler migration, dispossession from ancestral lands, and changing ways of life. As communities seek to nurture more resilient food systems, agroecology is emerging as a relevant food system framing to address these challenges as it balances new forms of sustainable food production with traditional food practices and connects them to on-going struggles for self-sufficiency and Indigenous food sovereignty. This article showcases insights from a community-driven, food systems planning project in Northwest Territories, Canada that incorporates agroecology rooted in Indigenous values, principles, and Traditional Knowledge of the region. Using participatory action research, the Ka’a’gee Tu First Nation (KTFN) designed a vision for their food system structured by the Community Agroecological Values Framework (CAVF). The CAVF, co-created with KTFN, builds on the community capitals framework and northern agroecology dialogues to foster a holistic approach to Indigenous food systems planning. Through a workshop, participatory mapping, and storytelling, community members reflected on existing food projects and provided input on future developments. KTFN used this process to connect their food system with multiple components of agroecology in the North, including land stewardship, sustainable livelihoods, cultural resurgence, social cohesion, good governance, and human capacity, aligning them with Dene values of holistic well-being for people and the environment. This article shares a case study of how KTFN is combining participatory, values- and place-based planning with agroecology to strengthen their food system, advance self-sufficiency, and promote food sovereignty in the face of climate uncertainties.
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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 | 0.023 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".