Farmer knowledge as formal knowledge: A case study of farmer-led research in Ontario, Canada
Bibliographic record
Abstract
Farmer-led research (FLR) is a process of inquiry wherein farmers use scientific methods to address their own on-farm curiosities and challenges in ways that are compatible with the scale and management style of their operations. With its flexible, adaptable, participatory, grassroots-oriented nature, FLR has typically been employed by farmers interested in ecological farming techniques and technologies, and evidence shows that it contributes to the adoption and improvement of ecological management practices across a range of contexts. Engagement in FLR initiatives has also been linked to positive social outcomes, including community-building, farmer empowerment, and enhanced capacity for leadership and collective action. In this paper, we present a case study of the Ecological Farmers Association of Ontario’s (EFAO) Farmer-Led Research Program (FLRP), which is currently one of relatively few FLR initiatives in North America. We draw on data from a participatory, mixed-methods research project. Our results highlight how the FLRP is enabling farmers to feel more knowledgeable, confident, motivated, and inspired to adopt and/or improve ecological practices on their farms, in part by supporting them in building robust social networks that align with their farming values and priorities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".