Exploring Indigenous food sovereignty and food environments characteristics through food interventions in Canada: a scoping review
Bibliographic record
Abstract
Indigenous food sovereignty (IFS) has the potential to reconnect Indigenous peoples in Canada to their food systems, reduce health problems and improve food security. Using PRISMA-ScR guidelines to search Medline, Web of Science, Embase and Cabi databases, this review sought to explore the characteristics of IFS promotion and the food environments involved through food and nutrition interventions in Indigenous communities in Canada. Data from 30 relevant studies published between 2004 and 2022 were included, analysed and synthesised using a thematic approach based on key IFS principles and a food environment typology. Most studies were conducted in urban contexts, mainly in provinces with the largest Indigenous populations. Local descriptions of IFS showed conceptual and operational similarities. Among the four key principles of IFS, the principle of participation was the most reported. Gardening, farming, hunting, fishing and gathering were the main food activities used to operationalise IFS in traditional and cultivated food environments. Several IFS facilitators and barriers were identified. The IFS movement that emerged from the literature in Canada advocates for a healthy and sustainable food system based on traditional beliefs and controlled by communities to ensure wellbeing and food security. This review provides evidence of converging visions for food autonomy despite the heterogeneity of Indigenous nations in Canada.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".