Learning Circles: A Collaborative Approach to Enhance Local, Healthy and Traditional Foods for Youth in the Northerly Community of Hazelton/Upper Skeena, British Columbia, Canada
Bibliographic record
Abstract
Youth health, long-term food sovereignty and the reclamation of traditional food-related knowledge are areas of concern within Indigenous communities in Canada. Learning Circles: Local Healthy Food to School (LC:LHF2S) built on an exemplar program in four predominantly Indigenous communities. In each, the initiative worked with interested community members to plan, implement and evaluate a range of activities aimed at enhancing access to local, healthy and traditional foods for schools and youth. This case study describes the context, process, outcomes and perceptions of implementation in one of the communities, Hazelton/Upper Skeena, located in northern British Columbia. Data were collected between 2016-2019 and included semi-directed interviews with community members and LCEF (n = 18), process reporting (e.g., LCEF reports, emails, conference calls and tracking data), photographs and video footage, and photovoice. Data were analyzed thematically. Hazelton/Upper Skeena has an active local and traditional food culture. Indigenous governance was supportive, and community members focused on partnership and leadership development, gardens, and food skills work. Findings point to strengths; traditional food, knowledge and practices are valued by youth and were prioritized. LC:LHF2S is a flexible initiative that aims to engage the broader community, and exemplifies some of the best practices recommended for community-based initiatives within Indigenous communities. Results indicate that a LC is a feasible venture in this community; one that can facilitate partnership-building and contribute to increased access to local and traditional food among school-aged youth. Recommendations based on community input may help the uptake of the model in similar communities across Canada, and globally.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".