Scale up of the learning circles: a participatory action approach to support local food systems in four diverse First Nations school communities within Canada
Bibliographic record
Abstract
BACKGROUND: Addressing Indigenous food security and food sovereignty calls for community-driven strategies to improve access to and availability of traditional and local food. Participatory approaches that integrate Indigenous leadership have supported successful program implementation. Learning Circles: Local Healthy Food to School is a participatory program that convenes a range of stakeholders including food producers, educators and Knowledge Keepers to plan, implement and monitor local food system action. Pilot work (2014-2015) in Haida Gwaii, British Columbia (BC), showed promising results of the Learning Circles (LC) approach in enhancing local and traditional food access, knowledge and skills among youth and adolescents. The objective of the current evaluation was therefore to examine the process of scaling-up the LC vertically within the Haida Nation; and horizontally across three diverse First Nations contexts: Gitxsan Nation, Hazelton /Upper Skeena, BC; Ministikwan Lake Cree Nation, Saskatchewan; and Black River First Nation, Manitoba between 2016 and 2019. METHODS: An implementation science framework, Foster-Fishman and Watson's (2012) ABLe Change Framework, was used to understand the LC as a participatory approach to facilitate community capacity building to strengthen local food systems. Interviews (n = 52), meeting summaries (n = 44) and tracking sheets (n = 39) were thematically analyzed. RESULTS: The LC facilitated a collaborative process to: (1) build on strengths and explore ways to increase readiness and capacity to reclaim traditional and local food systems; (2) strengthen connections to land, traditional knowledge and ways of life; (3) foster community-level action and multi-sector partnerships; (4) drive actions towards decolonization through revitalization of traditional foods; (5) improve availability of and appreciation for local healthy and traditional foods in school communities; and (6) promote holistic wellness through steps towards food sovereignty and food security. Scale-up within Haida Gwaii supported a growing, robust local and traditional food system and enhanced Haida leadership. The approach worked well in other First Nations contexts, though baseline capacity and the presence of champions were enabling factors. CONCLUSIONS: Findings highlight LC as a participatory approach to build capacity and support iterative planning-to-action in community food systems. Identified strengths and challenges support opportunities to expand, adopt and modify the LC approach in other Indigenous communities with diverse food systems.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.010 |
| 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".