The Community Citizen Scientist: Garden Stewards in First Nations Northern Ontario
Bibliographic record
Abstract
This case study explores the challenges and opportunities Garden Stewards – Community Citizen Scientists - during the implementation and management of the Community Gardens by the Braiding Food Systems (BFS) Project in First nations communities in Northern Ontario. Northern Ontario Indigenous communities, deeply impacted by climate change and economic marginalization, face significant food insecurity due to imposed market-based food systems. The BFS project seeks to address these challenges by revitalizing Indigenous seed use and supporting local food production. Six Garden Stewards, acting as Community Citizen Scientists, collected data on local food production in Pic Mobert, Rocky Bay, and Red Rock during the 2023-2024 growing season. Using the Participatory Citizen Science framework, which includes Learning for Action, Learning in Action, and Learning from Action, the study progressed through three stages: problem framing, research implementation, and impact. Problem framing involved community seed selection, identifying plant adaptability and growing patterns in contextual and unique Northern climates. Research implementation focused on collecting data on watering frequency, pest and disease presence, growing, nutrient deficiencies, and harvest timing. To address challenges, coaching activities and knowledge-sharing workshops were conducted. During Learning in Action, the team collaborated on gardening activities, exchanging insights on cultural practices and plant management for crops such as tomatoes, potatoes, and peppers. Reflective activities during Learning from Action identified lessons for future growing seasons. In the communities, Garden Stewards act as knowledge keepers, bond the communities, contextualize external knowledge to improve plant resilience and foster land-based practices through storytelling and observation. This participatory approach strengthens cultural identity and social cohesion, promoting well-being and resilience within the communities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".