Exploring Indigenous-informed contributions to decision-making to support improved food security in Canada: a scoping review
Bibliographic record
Abstract
Indigenous-informed food security initiatives are gaining global recognition for their potential to foster sustainable, community-minded solutions, while centering environmental stewardship, and the preservation of culturally significant foodways. Despite this growing aknowledgement, Indigenous involvement in decision-making related to improved food security in Canada remains underexplored. This review aims to contribute to deepened understandings of how Indigenous inputs are guiding current food security decision-making processes, and how these approaches are being applied in the context of mixed food systems throughout Canada. A systematic search of five online databases was conducted to examine the existing literature on Indigenous-informed food security efforts in Canada, exploring key themes, gaps and recommendations. Yielding a total of 1916 results, 39 of which were retained for further analysis, this search highlighted a broad swath of initiatives, programs, policies and strategies, developed by, in partnership with, or centering the perspectives of Indigenous communities. These existing initiatives frame how Indigenous groups are already guiding food security action in Canada, and what factors need to be considered to ensure on-going effectiveness. Findings highlight the need for more collaborative, cross-sectoral, community-minded food security initiatives, which integrate both support for Indigenous self-determination and recognize the validity of traditional knowledges within decision-making processes at all levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".