Perceptions of dairy cow–handling situations: A comparison of public and industry samples
Bibliographic record
Abstract
Inappropriate cattle handling poses a reputational threat to the dairy industry. To enhance social sustainability, handling practices must resonate with societal values about animal care. However, it has yet to be determined to what extent industry and public stakeholders differ in their perception of common cattle handling situations. We administered an online survey to samples of dairy industry (IND) and public (PUB) stakeholders to examine how they perceive a variety of cow-handling scenarios ranging from positive to negative in terms of effects on animal welfare. Participants were presented with 12 brief videos depicting a range of realistic cow-handling situations and responded to measures designed to assess their attitudes and beliefs about each scenario, their perception of the emotional response of the cows depicted in each scenario, as well as their own personal emotional response. Preexisting beliefs about cow treatment on US dairy farms and demographic data, including self-reported dairy consumption, were also collected and analyzed. Before viewing the videos, 52.9% of PUB (vs. 79.0% of IND) believed cows were treated well while 27.2% (vs. 9.0% of IND) believed cows were treated badly. Within IND, believing cows were treated badly was more common among nonwhites, those with greater formal education, more liberal politics, or from urban or suburban environments. In PUB, female and younger participants were more likely to believe cows were treated badly before viewing the videos. In both samples, participants with more positive preexisting beliefs about dairy cow treatment in the US reported consuming dairy products more frequently. In both PUB and IND, scenarios which were rated more positively for attitudes or for the cows' or respondents' emotional experiences were also perceived as more common. Within a given cow-handling scenario, qualitative attitudes (i.e., a positive, negative, or neutral valence) did not differ between the samples. In both samples, at the participant level, overall attitudes toward cow-handling scenarios were highly correlated with both their personal emotional response to the scenario and their perception of the cows' emotional responses. Although the participants' overall personal emotional responses did not differ between the samples, IND rated cows as experiencing more negative emotions overall. The consensus between industry and public stakeholders around dairy cow-handling practices observed in this study could provide a common starting point for addressing other, more contentious animal welfare issues.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".