Food programs in Indigenous communities within northern Canada: A scoping review
Bibliographic record
Abstract
Abstract Recognizing that limited literature exists regarding food programs in northern Indigenous communities within Canada, this study draws on a range of sources to map and characterize existing food programs in these contexts. A secondary aim assessed the extent to which traditional food was offered through the identified programs, which has implications for cultural appropriateness and, in turn, food sovereignty. Peer‐reviewed articles and grey literature published between 2000 and 2022 were examined. Frameworks to guide methodologies include PRISMA‐ScR, Arksey and O'Malley, Levac et al., and Godin et al.'s grey literature search strategy. Inclusion criteria were food programs located north of the Northern Boundary Line, programs providing food access, and programs serving Indigenous communities. Data were synthesized based on program type, target population, and whether the program offered or incorporated traditional food. The review yielded 30 records wherein 46 unique food programs were identified and characterized into eight distinct program types. Program success of the identified programs depended on funding availability and continuity, staff/volunteer availability and retention (including program champions), and types of policies that impact traditional food provision. Findings are valuable to organizations and communities interested in using food programs to support Indigenous food security and sovereignty efforts.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.031 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".