Public concerns about dairy-cow welfare: how should the industry respond?
Bibliographic record
Abstract
Common practices on dairy farms have fallen out of step with public values, such that the dairy industry has now become a target for public criticism. In the present paper, we describe some of the forces that have led to the current situation, and various potential methods to rectify the situation. One approach is to shield industry practices from public scrutiny, for example, by using ‘ag-gag’ legislation to stem the flow of videos exposing contentious practices. Another is to educate members of the public so that they better understand the nature of these practices and the reasons that they are used on farms. The literature we reviewed indicated that neither of these approaches is likely to be successful. Instead, we suggest that the dairy industry needs to develop methods of meaningful two-way engagement with concerned citizens, including research using social-science methods to document the values of different stakeholders and examine approaches to resolving conflicts. We also reviewed how biological research can help resolve issues, for example, by developing rearing systems that address public concerns around freedom of movement and social contact without putting animals at an increased risk of disease. We end with a discussion of how policy efforts by the dairy industry can be used to ensure compliance with commonly accepted standards, and more ambitiously, develop a common vision of dairying that positions the industry as a leader in animal welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".