The Community Citizen Scientist in First Nation Communities Subtitle: The Garden Stewards in First Nations in Northern Ontario, Case Study
Bibliographic record
Abstract
This case study explores the Braiding Food Systems project, which researches and builds capacity with First Nations in Northern Ontario. By combining Indigenous knowledge with scientific insights, the project supports community-driven food system activities, enhancing local ownership and resilience in food production. It evaluates the Participatory Citizen Science Approach, involving Garden Stewards in three First Nations to promote sustainable food production, strengthen community capacity, and support food sovereignty. Over five months in 2024, five Garden Stewards participated in activities, monitored gardens, and collected plant growth data. The methodology followed Indigenous Food Systems principles, using a Soft Systems Thinking approach within a One Health framework, connecting human, animal, environmental, and socio-cultural health. The project employed a three-phase learning framework: (1) Learning for Action (capacity development), (2) Learning in Action (data collection), and (3) Learning from Action (reflection and refinement). It identified opportunities and challenges in promoting sustainable food production in northern climates, yielding key outcomes such as improved plant resilience and pest management strategies. Integrating Indigenous knowledge with scientific insights proved essential for food security and sovereignty. Community engagement was vital for local ownership and sustainability. Nonetheless, communication issues, data collection challenges, and technology application difficulties arose in remote areas. This study illustrates citizen science's potential to enhance understanding and empower communities while highlighting the importance of merging Indigenous knowledge with scientific methods to boost adaptive capacity and promote food sovereignty. Findings stress the need for context-specific strategies to overcome communication and technology barriers for sustainable food systems in Indigenous communities across Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".