Seeding agroecology through new farmer training in Canada: knowledge, practice, and relational identities
Bibliographic record
Abstract
As a concept, agroecology emphasises the interweaving of scientific and traditional ecological knowledge and is evolving in conjunction with farmer-led social movements from around the world addressing the health, equity and ecological sustainability of food systems. In Canada, many new agroecological farmers come from non-farming backgrounds and are finding limited training opportunities and support structures. While there is a growing literature on the evolution of agroecology, there is limited research on the existence and impact of training programmes on the subject-formation of new farmers. In this paper, we consider the subject-formation of new agroecological farmers through a case study of the Everdale Community Learning Centre, one of Canada’s only agroecological farm schools. In particular, we explore how the knowledge, practice, and relational identities of participating graduates are informed by and build on the science, practice, and movement of agroecology. Drawing on a survey and interviews with past participants, we found that Everdale’s education programme contributes to an agroecological subject-formation by promoting the co-creation of place-based agricultural knowledge; teaching the complexities of agroecology practice through both experiential and theoretical training; and, building a supportive community of peers. We conclude with reflections on ways to encourage a greater diversity of new farmer entrants and opportunities to support training programme graduates in establishing successful farms. These findings provide insight into developing new agroecological farmers and supporting the growing agroecological movement in 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 | 0.029 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".