Can selling traditional food increase food sovereignty for First Nations in northwestern Ontario (Canada)?
Bibliographic record
Abstract
The disparity between rates of food insecurity experienced in households across Canada (8.3%) and in Indigenous households specifically (nearly half) is alarming. Many previous studies have demonstrated the physical, spiritual, mental, social and emotional benefits of consuming traditional foods (primarily wild animal food sources and wild edible plants), yet many Indigenous peoples in northern Ontario feel they do not have access to enough of them. Our research engaged in conversation with sixteen participants from four different First Nations communities in northern Ontario to explore the potential application of Greenland’s “Country Food Market” (CFM) as a model to increase accessibility of traditional food while maintaining community sovereignty over the resource. In this model, full-time hunters are financially sustained through selling their harvest at local markets. While participants were curious about the potential an economy around traditional food could have for improving access, this was tempered by cultural ethics, teachings and laws which instruct hunters to share their food and by concerns of resource overexploitation. As our research confirms, conversations and actions must move away from a binary approach to the question—either to sell or not to sell—and move toward a diverse range of economic models that center Indigenous peoples’ sovereignty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".