“We are stewards and caretakers of the land, not exploiters of resources”: A qualitative study exploring Canadian farmers’ perceptions of environmental sustainability in agriculture
Bibliographic record
Abstract
Environmental sustainability in agriculture is a key component of discussions to address the current climate crisis; unfortunately, many people (including researchers) presume that only certain types of agriculture (e.g., organic, local) are environmentally sustainable. Non-farmers also fail to acknowledge that many farm practices, including grazing animals, mitigate climate change. Farmers' perceptions about environmental sustainability are important because their livelihoods, and those of future generations, depend on their commitment to sustainable environmental practices. The purpose of this qualitative research was to understand Canadian farmers' perceptions of environmental sustainability, how they are implementing strategies that contribute to sustainable food production, and the challenges they face. Fifty-two farmers, representing 48 farms and over 1000 years of farming experience, participated in comprehensive in-depth interviews. Four farms were in British Columbia; 13 in the Prairies; 26 in Central Canada; and five in Eastern Canada. A wide variety of farm types (e.g., fruit/vegetables, livestock, grains) and sizes (2 to 6500 acres) were included in the study. Farmers' perceptions of environmental sustainability coalesced into four main themes: (1) definitions of sustainability and environmental sustainability, (2) current practices, (3) farming as an identity, and (4) challenges. Many participants explained that they already use sustainable practices and technology, contrary to prevailing opinion that entire food systems need to be transformed to be sustainable. As new agricultural policies and educational curricula are developed, information provided to students, policy makers, and the public must be accurate, balanced, evidence-based, and respectfully consider all perspectives, especially those of farmers.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".