Cultivating Knowledge: Agrarian Science and Ecological Engagements in Southern Ontario Agriculture
Bibliographic record
Abstract
Agriculture and climate change are interconnected processes, with agriculture implicated in rising green house gas emissions, deforestation, soil and water pollution, and reductions in biodiversity. Conversely, changes within ecology (including a warming climate), alter growing conditions for farmers. Farmers face changes in both temperature and precipitation, as well as an increase in adverse weather events that significantly threaten productivity and livelihoods.\nBased on 40 unstructured interviews as well as informal conversations conducted among farmers in southern Ontario, Canada between the spring of 2014 and the winter of 2017, this dissertation seeks to contribute to a growing body of work that focuses on the complex factors that shape farmer decision making in the face of environmental and climate concerns, while also paying particular attention to the role of farmers in southern Ontario as knowledge creators. This distinction is important in that it acknowledges that farmers are not just passive in the processes of adaptation, but are active in attempting to enhance their capacity and resiliency to climate change by taking part in practical experimentation and knowledge sharing.\nIn a departure from much of the climate change literature in Canada, my analysis attends to the complex relationships among species that shape both farmer identity and ecological knowledge. Multi-species intimacies are integral to farming life and shape farmer decision making in unpredictable ways. This dissertation also critically engages with the concept of “science-based” research. With an emphasis on farmers’ contributions to enhancing resiliency to climate change while increasing environmental sustainability through agrarian science, this study examines the politics surrounding the concept of “science” and how it is manifested in discussions of agriculture and the environment in the Canadian context.\nFarmers in southern Ontario are not just growing crops, they are “cultivating knowledge” by actively seeking out multiple sources of information, taking part in practical experimentation, and sharing knowledge with other farmers. This dissertation documents some of the ways these processes are unfolding as southern Ontario farmers seek to acquire and develop new methods of growing food that will help them adapt to the complex challenges associated with climate change.
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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 | 0.022 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".