American and German attitudes towards cow-calf separation on dairy farms
Bibliographic record
Abstract
Public concerns regarding the quality of life of farm animals are often focused on specific practices such as separating the cow and calf immediately after birth. The available scientific literature provides some evidence in support of this practice (including reduced acute responses to separation when it does occur), as well as evidence of disadvantages (such as increased risk of uterine disease in cows). The aim of this study is to systematically examine public views around this practice. Specifically, this study analyzes the views of American and German citizens to separation of cow and calf at birth using a quantitative segmentation approach. Although the majority of participants opposed early separation, a small proportion of our sample supported the practice. According to participants' preference for early and later separation and their evaluation of different arguments for both practices, three clusters were identified. US participants were more likely to support early separation compared to German participants. The arguments presented for and against both practices caused different reactions in the three clusters, but did not appear to sway the opinions of most participants. The results show considerable opposition to the practice of early separation in large parts of the sample and suggest that the dairy industry should consider approaches to address this concern.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".