Perspectives of western Canadian dairy farmers on the future of farming
Bibliographic record
Abstract
Similar to the situation in many countries, the dairy industry in Canada is challenged by the need to adapt to changing societal demands. An industry-led initiative (Dairy Farmers of Canada's proAction Initative, known as proAction) was developed to respond to this challenge, providing mandatory national standards for on-farm practices. Farmers are more likely to follow such standards if they are aligned with their values and beliefs. The aim of this study was to better understand farmers' perspectives on the future of the Canadian dairy industry, with a focus on the role of mandatory policies such as those related to proAction. Seven focus groups were conducted, with discussions based on the principles of appreciative inquiry. Participants were each asked to write down key words that represent the "must-haves" on dairy farms in 20 yr from now. Although participants were encouraged to focus on aspects directly related to animal care, all answers were accepted. Key words were then used to facilitate a discussion and elicit ideas on how to achieve these must-haves. Particular focus was on the direction that participants believed policy should take to meet these goals. Explorative qualitative analysis was used for the written key words, and transcripts of the audio-recorded focus group discussions were analyzed using thematic analysis. Examples of farm-specific considerations that were raised as future must-haves of animal care on dairy farms included cow comfort, employee management, responsible health management, and use of advanced thechnologies. Participants agreed that objectives can only be achieved through collaboration among farmers and between farmers and researchers, and they regarded citizen education as a promising approach to align differing expectations of the public and farmers. Citizen trust in the dairy industry was considered a must-have, and participants believed that one of the benefits of mandatory policies for animal care is their potential to increase trust. These results may help guide the development of new animal care policies and increase understanding of the perceived legitimacy of new policies by dairy farmers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".