Public attitudes toward different management scenarios for “surplus” dairy calves
Bibliographic record
Abstract
As awareness grows, some traditional management practices used by the dairy industry will be questioned by members of the public. Therefore, to maintain its social license to operate, the industry needs to account for public perspectives when developing future directions. Our aims were to assess attitudes of members of the public toward the management of surplus calves not needed for milk production on dairy farms, and to assess how specific calf management practices might influence these attitudes. A mixed-methods questionnaire was developed and distributed online in the United States and in Canada. After reading an introductory paragraph stating that surplus calves are generally used for meat production, participants were randomly allocated into groups and read 1 of 4 scenarios that described different surplus calf management practices in more detail. The scenarios followed a 2 × 2 factorial design, and the factors that differed were the calf's age at slaughter (≤2 wk vs. ≥12 mo), and whether the calf was separated from the cow at birth or sometime later. Data representative of key census demographics from 998 participants were analyzed. Quantitative data analysis included descriptive statistics, nonparametric tests, generalized partial credit models, and linear regression models. For qualitative data, we used reliability thematic analysis. Overall, participants were slightly positive in their attitudes toward the introductory paragraph, and participants in the groups in which the calf was slaughtered after 12 mo of age often specifically linked their acceptance of the practice to the fact that the calves' lives had a purpose (i.e., contributing meaningfully to the beef supply). In contrast, only 3% of the participants regarded a slaughter age of <1 mo as appropriate. Participants in the groups in which calves were slaughtered within 2 wk after birth had more negative attitudes, and these attitudes declined even further when the calf was separated from the cow soon after birth. Besides the 2 main factors (age at slaughter and cow-calf separation), information on pasture access, the healthiness of the meat from the calves, and the exact age of slaughter were also considered important by participants to make a more informed decision about their view on surplus calf management. Overall, our results indicate that failure by the dairy industry to provide assurances that excess dairy calves have a reasonable length of life and that this life has purpose (i.e., contributes to the beef supply chain) places the industry at odds with public values. Also, as awareness grows, the practice of early cow-calf separation will be increasingly questioned by the public; failure to begin discussions on this topic will increase the risk that future decisions about this topic will be made in the absence of the farmer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".