Pathways to Revitalization of Indigenous Food Systems: Decolonizing Diets through Indigenous-focused Food Guides
Bibliographic record
Abstract
The 2019 Canadian Food Guide (CFG) was launched in January 2019 with a promise to be inclusive of multicultural diets and diverse perspectives on food, including the food systems of Indigenous communities. Some scholars argue that federally designed standard food guides often fail to address the myriad and complex issues of food security, well-being, and nutritional needs of Canadian Indigenous communities while imposing a dominant and westernized worldview of food and nutrition. In a parallel development, Indigenous food systems and associated knowledges and perspectives are being rediscovered as a hope and ways to improve current and future food security. Based on a review of relevant literature and our long-term collaborative learning and community-based research engagements with Indigenous communities from Manitoba, we propose that Indigenous communities should develop their food guides considering their contexts, needs, and preferences. We discuss the scope and limitations of the most recent Canadian food guide and opportunities to decolonize it through Indigenous food guides, including their potential benefits in enhancing food security and well-being for Indigenous communities. We propose to design and pilot test such Indigenous food guides in communities Fisher River Cree Nation in Manitoba as community-based case study research that supports Indigenous-led and community-based resurgence and decolonization of food guides.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".