Learning circles: an adaptive strategy to support food sovereignty among First Nations communities in Canada
Bibliographic record
Abstract
Indigenous communities in Canada are concerned about the health of their youth and the reclamation of traditional food-related skills amongst their people. Food sovereignty has an integral role in food and nutrition security, and the path to Indigenous self-determination. Learning Circles: Local Healthy Food to School (LC:LHF2S) was a community engagement model that aimed to enhance access to local, healthy, and traditional foods for youth. In each of four First Nations communities, a Learning Circle Evaluation Facilitator worked to plan and implement activities, build on community strengths, and promote partnerships. This paper describes how the model was perceived to support food sovereignty. Data included interviews, process reporting, and school surveys, and was analyzed according to pillars effective for the development of food sovereignty in Indigenous communities. Goals set by two communities incorporated food sovereignty principles, and in each community capacity-building work furthered the development of a more autonomous food system. There were many examples of a transition to greater food sovereignty, local food production, and consumption. Indigenous governance was an important theme and was influential in a community’s success. The model appears to be an adaptable strategy to support the development of food sovereignty in First Nations communities. Novelty: LC:LHF2S was a community engagement model that aimed to enhance access to local, healthy, and traditional foods for youth. The model is an adaptable strategy to support the development of food sovereignty in First Nations communities. There were many examples of a transition to greater food sovereignty, local food production, and consumption.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".