Canadian farmers’ perceptions of social sustainability in agriculture
Bibliographic record
Abstract
Sustainable food production is an important part of dietetic education and training; however, the focus in the dietetic sphere is often on the environmental aspect. Understanding the multi-dimensional nature of sustainability can enhance dietetic students' sustainability competences-such as empathy and change of perspective, systems thinking, and critical thinking and analysis-to help them in their future careers and strengthen their position in society as trusted and knowledgeable food and nutrition professionals. Enhancing public understanding of sustainable food production is imperative as populations become more urban, are less connected to agriculture, and have expectations for sustainably grown/raised food, often without knowing current food production practices or the multiple aspects of sustainability that must be in place for farmers to meet those demands. The goal of this research was to understand Canadian farmers' perceptions of environmental, economic, and social aspects of sustainable food production. Employing a descriptive qualitative approach and constant comparative analysis, four food and nutrition researchers analyzed interviews from 52 farmers from across Canada. Participants had to be English-speaking, produce food through farming on land, and own or rent the land on which they farm. Telephone/video interviews revealed five overarching social themes: (1) the importance of community and social capital, (2) public perception and social license to operate, (3) lack of infrastructure, and (4) deep connections to personal lives. The final theme, mental health issues (5), reflected the consequences of the multiple sources of stress that can undermine the social sustainability of farmers, farm communities, and food production. These findings may help various audiences appreciate the multiple dimensions of sustainable food production; reflect on their values, perceptions, and actions with regard to agriculture; and enhance their compassion and empathy for all 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".