The welfare of dairy cattle: perspectives of industry stakeholders
Bibliographic record
Abstract
The aim of the current study was to describe the perspectives of stakeholders within the dairy industry on key issues affecting the welfare of dairy cattle. A secondary aim was to examine if these stakeholders believed that people outside of the industry should also have a voice in formulating solutions to these issues. Five heterogeneous focus groups were conducted during a dairy cattle industry meeting in Guelph, Canada in October 2012. Each group contained between 7–10 participants and consisted of a mix of dairy producers, veterinarians, researchers, students, and industry specialists. The 1-h facilitatorled discussions focused on participants’ perceptions of the key welfare issues and the role of different stakeholder groups in addressing these concerns. Discussions were audio-recorded and transcribed verbatim, and the resulting transcripts coded and the themes identified. Lameness was uniformly recognized as the most important welfare issue facing dairy cattle; cow comfort, painful procedures (like dehorning) and production diseases (like mastitis) were also commonly discussed. Participants had mixed views on the roles of different stakeholders in formulating solutions. Most felt that producers and others working within the dairy industry (particularly veterinarians) should be primarily responsible, but many participants acknowledged that the general public, as consumers and as citizens, also play an important role. Participants seemed to focus on a two-fold knowledge deficit - first between researchers and producers, and second between dairy industry groups and the public - and agreed that improved knowledge translation was required to develop solutions to welfare concerns. These results indicate that many people within the dairy industry see value in more inclusive engagement with non-industry stakeholders about dairy cattle welfare. Future work will assess perspectives of people outside of the dairy industry to identify areas of shared concern and provide a basis for policy solutions that better incorporate societal values.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".