Animal welfare information frames US public perceptions of precision technology use on dairy farms
Bibliographic record
Abstract
With increased interest in precision livestock farming (PLF) by dairy producers, a clearer understanding of public perceptions is needed to ensure that use of these technologies is socially sustainable. This study sought to (1) characterize public perceptions toward precision dairy technologies (PDT), trust in farmers using PDT, and likelihood to purchase (LTP) dairy produced with PDT; (2) describe how these perceptions may be influenced by information framing about the potential effects of PDT use; and (3) evaluate the influence of demographic characteristics of the participant sample. In an online survey distributed to US residents (n = 2,365), participants randomly received 1 of 8 vignettes, each containing either positively or negatively valenced information about hypothetical PDT effects on human-animal relationship, data transparency, and animal welfare. Perceptions toward PDT were assessed with Likert item questions before and after vignette exposure and through representational thematic text analysis of open-ended responses (n = 80 randomly selected) justifying the Likert responses. Linear models were selected using backward selection to assess significant predictors of baseline and shifts in perceptions toward PDT, farmer trust, and LTP. Baseline perceptions toward PDT, trust, and LTP were generally positive, with more positive perceptions associated with higher incomes, rural backgrounds, and dairy consumers more knowledgeable about production; in contrast, baseline perceptions were more negative among participants with higher levels of concern toward animals. Across the sample, participants experienced slight negative shifts in PDT perceptions after vignette exposure, with those who indicated a voting preference for the Democratic party (e.g., politically liberal) and who had more positive attitudes about animal protection more vulnerable to negative perception shifts. Generally, information about data transparency did not shift perceptions of PDT in either direction. However, the valence of animal welfare information strongly predicted the direction of perception shifts, such that negative information was particularly influential in generating worsened perceptions of PDT, even if participants received positive information about other attributes. Similarly, the perceived effect of PDT on cows was the most frequently raised theme in the qualitative responses, though participants also focused on ethical acceptability of the technology itself and how it might affect farmers. We suggest that for the dairy industry to maintain its social license to operate, it will benefit from more closely engaging with public concerns regarding potential effects of PDT on animal welfare and the relationship between farmers and animals.
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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 | 0.005 | 0.016 |
| 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 | 0.003 | 0.002 |
| 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".