Perspectives of dairy farmers on positive welfare opportunities for dairy cows in Ontario, Canada
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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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 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 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".