Brazilian Citizens: Expectations Regarding Dairy Cattle Welfare and Awareness of Contentious Practices
Bibliographic record
Abstract
The primary aim of this study was to explore attitudes of urban Brazilian citizens about dairy production. A secondary aim was to determine their knowledge and attitudes about four potentially contentious routine dairy cattle management practices: early cow-calf separation; zero-grazing; culling of newborn male calves; and dehorning without pain mitigation. To address the first aim 40 participants were interviewed using open-ended semi-structured questions designed to probe their views and attitudes about dairy production in Brazil, and 300 participants answered a questionnaire that included an open-ended question about the welfare of dairy cattle. Primary concerns reported by the participants centered on milk quality, which included the rejection of any chemical additives, but also animal welfare, environmental and social issues. The interviewees rarely mentioned animal welfare directly but, when probed, expressed several concerns related to this topic. In particular, participants commented on factors that they perceived to influence milk quality, such as good animal health, feeding, clean facilities, and the need to avoid or reduce the use of drugs, hormones and pesticides, the avoidance of pain, frustration and suffering, and the ability of the animals to perform natural behaviors. To address our second aim, participants were asked questions about the four routine management practices. Although they self-reported being largely unaware of these practices, the majority of the participants rejected these practices outright. These data provide insight that animal welfare may be an important issue for members of the public. Failure to consider this information may increase the risk that certain dairy production practices may not be socially sustainable once lay citizens become aware of them.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".