Envisioning Community Economic Development Through an Indigenous-Led Social Enterprise in Ka’a’gee Tu First Nation, Northwest Territories
Bibliographic record
Abstract
The Ka’a’gee Tu First Nation, in Kakisa, Northwest Territories, is cultivating food to strengthen their food systems against multifaceted threats posed by colonization, climate change, and socioeconomic disparities. Community efforts to grow food are new and stand as an adaptation response to their changing food system. Although establishing food-growing initiatives has been a gradual process, their success is now evident with substantial quantities of food being produced. This research addresses the need for a sustainable food distribution model in Kakisa to ensure food is accessible to the community. Using a participatory action research approach, community members shared their vision, leading to the exploration of an Indigenous-led economic model merging Western approaches with Indigenous values. Kakisa’s enterprise will support food distribution systems, including a store, and act as a space to host social gatherings, facilitate Traditional Knowledge workshops, and share food. The community’s vision of an Indigenous-led social enterprise embodies a holistic approach to economic development that emphasizes social bonding and community well-being over pure economic activities. Accomplishing this vision requires continuous efforts toward fostering collaboration, nurturing cultural resurgence, and empowering Indigenous leadership within economic development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".