Public attitudes toward dairy farm practices and technology related to milk production
Bibliographic record
Abstract
Dairy farm systems have intensified to meet growing demands for animal products, but public opposition to this intensification has also grown due, in part, to concerns about animal welfare. One approach to addressing challenges in agricultural systems has been through the addition of new technologies, including genetic modification. Previous studies have reported some public resistance towards the use of these technologies in agriculture, but this research has assessed public attitudes toward individual practices and technologies and few studies have examined a range of practices on dairy farms. In the present study, we presented participants with four scenarios describing dairy practices (cow-calf separation, the fate of excess dairy calves, pasture access and disbudding). Citizens from Canada and the United States (n = 650) indicated their support (on a 7-point scale) toward five approaches (maintaining standard farm practice, using a naturalistic approach, using a technological approach, or switching to plant-based or yeast-based milk production) aimed at addressing the welfare issues associated with the four dairy practices. Respondents also provided a text-based rationale for their responses and answered a series of demographic questions including age, gender, and diet. Participant diet affected attitudes toward milk alternatives, with vegetarians and vegans showing more support for the plant-based and yeast-based milk production. Regardless of diet, most participants opposed genetic modification technologies and supported more naturalistic practices. Qualitative responses provided insight into participants' values and concerns, and illustrated a variety of perceived benefits and concerns related to the options presented. Common themes included animal welfare, ethics of animal use, and opposition toward technology. We conclude that Canadian and US citizens consider multiple aspects of farm systems when contemplating animal welfare concerns, and tend to favor naturalistic approaches over technological solutions, especially when the latter are based on genetic modification.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".