Implementation of the Learning Circle: Local Food to School Initiative in the Island Communities of Haida Gwaii, British Columbia, Canada—a Descriptive Case Study
Bibliographic record
Abstract
Background: Indigenous communities in Canada are concerned about long-term food sovereignty and the reclamation of traditional food-related skills among their people. Learning Circles: Local Food to School (LF2S) was an innovative community-based project that worked to promote multisectoral partnerships, focused on food. Guided by a facilitator, the Learning Circle (LC) brought together interested community members to plan and implement activities aimed at enhancing access to local, healthy, and traditional foods for school communities. Objectives: The article describes the context, process, and food-related outcomes of the LC in Haida Gwaii (HG), British Columbia, Canada and evaluates perceptions of the transition in local food systems, food literacy, and community capacity associated with the LC. Methods: = 75). Data were analyzed thematically. Results: HG has a rich food environment and a vibrant local and traditional food culture. A variety of local food-related activities had been taking place before the launch of the LC in 2013, and by 2016 the initiative was firmly established. Between 2016 and 2019, activities in HG focused on schools (e.g., sourcing local, healthy food and developing traditional skills through schools) and pioneering local food pantries. Participants valued increased access to local and traditional foods and opportunities to build youth knowledge and skills. Noted successes of the LC process included transitioning to Haida leadership and fostering relationships. Conclusions: The inherent flexibility of the LC model means that communities can prioritize activities of interest. The evolution of the LC model in HG is an inspiration for other communities working to enhance food sovereignty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".