'Walking through your old way of thinking': the learning dimension of farmers' transitions to sustainable agriculture
Bibliographic record
Abstract
Many farmers express significant concern about the chemical use on their farms---a common trait of North American agriculture. Nonetheless, most farmers continue to practise chemical agriculture. This dissertation focuses on farmers who have made a transition from chemical to sustainable agriculture in the province of Alberta. Sustainable agriculture, in the context of this dissertation, is an approach to farming that considers the farm as an ecosystem with interventions geared towards treating root causes rather than symptoms. This is contrasted with chemical agriculture. Within the context of this adult education thesis, farmers' transitions are conceived as an informal learning project. The central argument is that farmers' learning is transformative to the extent that it transcends acquiring new farming skills and knowledge to encompass an understanding of the chemical agrifood system as fundamentally socially unjust and environmentally destructive. The limited previous research into farmers' transitions focuses on the important ecological aspects of farm transition, often to the exclusion of social aspects. This thesis makes a contribution to understanding farmers' motivations for transition, the process of their informal transformative learning and the various forms of civic engagement that result. The data show that farmers followed one of four paths to sustainable farming practices: incomplete transformative learning; conscientization through sustainable agriculture; concurrent transformations; and closing a values-action gap. In all but the first of these, farmers had a counterhegemonic understanding of both their farming practices and their civic engagement. A major form of farmers' engagement is through the articulation of a sustainable agriculture standpoint---a vision for sustainable farming practices as part of a sustainable agrifood system. Farmers are also involved formally and informally in myriad other activities in their rural communities and beyond through business, non-profit, and state organizations. Situating the research in the province of Alberta gives an interesting insight into farmers' different forms of dissent because of the political realities of this context. The theoretical framework combines insight from the political economy of sustainable agriculture, hegemony and counterhegemony, feminist standpoint theory and transformative learning and conscientization. Data were collected through a series of semi-structured interviews with sustainable farmer families in Alberta.
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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 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".