INTEGRATING INDIGENOUS VALUES AND COMMUNITY STRENGTHS TO ACHIEVE INDIGENOUS FOOD SOVEREIGNTY AND COMMUNITY WELL-BEING IN THE KA’A’GEE TU FIRST NATION, NORTHWEST TERRITORIES
Bibliographic record
Abstract
Northern Indigenous communities face disproportionate impacts from interconnected challenges related to climate change, food security, health, and cultural preservation, that threaten traditional food systems and practices. While there is a growing recognition of the importance of community-driven solutions, existing frameworks fail to integrate Indigenous values and local priorities effectively. This research addresses this gap by exploring how the Ka’a’gee Tu First Nation (KTFN) is responding to these crises through climate change adaptation efforts focused on food system sustainability as a pathway to self-sufficiency and community well-being. To contribute to these efforts, this dissertation uses Participatory Action Research to facilitate community-driven planning and action intended to improve access to healthy foods, foster knowledge-sharing, create employment opportunities, and encourage engagement in local food governance. This research introduces a novel framework, the Community Agroecological Values Framework (CAVF), which integrates the Community Capitals Framework and Agroecology to prioritize Dene values in the planning process. I characterize Kakisa’s food system, map traditional food-sharing networks, and identify critical resources and synergies to strengthen community and regional food systems. Findings highlight the contributions of Kakisa’s traditional food-sharing networks in supporting food sovereignty, cultural preservation, and social cohesion, while identifying barriers such as resource limitations and governance misalignments. This work provides recommendations for fostering sustainable food systems in northern communities by integrating traditional and modern practices, regional collaboration, and systemic change. Future research should focus on enhancing governance structures, strategies for building local capacity, and fostering regional partnerships to ensure long-term viability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".