Perspectives of dairy farmers on positive welfare opportunities for dairy cows in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Positive experiences offer opportunities to improve the experiences of animals through positive affect, beyond the absence of negative experiences such as illness or pain. The objective of this study was to describe the perspectives of dairy farmers regarding positive welfare opportunities for dairy cows and calves. Five focus groups were held with dairy farmers (n = 27) in Ontario, Canada. Audio recordings of the discussions were transcribed verbatim, and applied thematic analysis was used to analyze the data. Participants initially focused discussion on pasture access, cow-calf contact, and group housing of calves. Two themes were identified from the data: 1) tacit expertise of farmers and 2) influences on farmer choice. Participants invoked their expertise and had conflicting opinions on how various positive opportunities could affect cattle health and welfare. There were divergent views when discussing dairy farming in general. However, when speaking specifically about their own farm, participants were reluctant to implement positive opportunities, citing risks of decreased milk production and avoidable health problems. Autonomy to choose which positive opportunities best suited farm-specific management and financial situations was preferred to regulation. Finally, participants prioritized minimizing negative experiences for cows and calves but maintained aspects of positive welfare (e.g., described as happy, content, or autonomy) as important characteristics of a cow’s life.
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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 | 0.001 |
| 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 it