Tail docking dairy cattle: Responses from an online engagement1
Bibliographic record
Abstract
Tail docking remains a common practice on dairy farms in the United States. This paper describes the results of an online engagement designed to create discussion on tail docking, to document the reasons participants put forward for and against the practice, and to compare these reasons with the literature available on this topic. A total of 178 people responded; 30% were producers, 23% were veterinarians, 25% had no experience with the dairy industry, and 22% included a mixture of teachers, students, and industry professionals. Approximately 79% of participants were opposed to docking. Responses varied with participant demographics (e.g., females were more likely than males to oppose docking), but in every demographic subgroup (e.g., by sex, age, country of origin, and dairy production experience), the majority of respondents were opposed to tail docking. Common reasons for opposition to docking included the lack of scientific evidence that docking improves cleanliness or udder health, that docking is painful for cows, that docking is unnatural, and that tails are important for controlling flies. Some respondents in favor of docking cited cow cleanliness as an issue, despite the scientific evidence showing no positive effect of docking on cow cleanliness or udder health. Additional reasons included protecting producer safety. These results illustrate the range of reasons that are cited for supporting and opposing tail docking. This approach can be used to better target outreach efforts (e.g., improving farmer education on the lack of positive effects of docking on cleanliness and udder health while addressing concerns about producer safety). More generally, this type of online discussion provides a safe and productive format for discussions about contentious issues in the dairy industry and provides a mechanism for producers, industry professionals, and the public to share perspectives on these topics.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".