Understanding the Dynamics of Food and Economic Development in the Community of Kakisa, Northwest Territories, Canada
Bibliographic record
Abstract
For Indigenous Peoples, the land embodies a source of cultural and spiritual well-being that is linked to their way of life and identities through divine and traditional connections. Unfortunately, colonial powers, the global food system, socioeconomic inequalities and environmental degradation are impacting northern Indigenous lands and food systems, leading to food insecurity. Nevertheless, communities are still thriving in the face of adversity. In the Northwest Territories, on the lands of Ka’a’gee Tu First Nations (KTFN) in the community settlement of Kakisa, people are growing food through vegetable gardens and greenhouses to support their vision of a resilient food system.\nThrough partnerships with researchers and other communities, residents of Kakisa have been learning to grow food in ways that connect to the land and its traditional food system. After several successful years of growing food, there is a possibility of growing more food than the community needs. Community members are determining what to do with a surplus of food and considering adopting a store (food hub/social enterprise) as a method of distributing and selling produce to outside communities.\nUsing one-on-one interviews, community members were asked to envision what a food distribution model would look like in Kakisa. Community partners revealed that a new enterprise should be centered on food sharing, building relationships, and empowering women while enhancing socioeconomic opportunities through the creation of jobs. Thereby, an Indigenous-led enterprise will help to improve socioeconomic conditions, mitigate the effects of climate change, increase access to food, and foster self-determination and Indigenous food sovereignty, while enhancing Indigenous culture. This thesis also aims to present valuable lessons and ethical considerations for non-Indigenous enterprises, encouraging an alternative pathway beyond the conventional 'capitalistic colonial' entrepreneurial system.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".