Is bigger better? Farm size and animal welfare
Bibliographic record
Abstract
The intensification of agriculture is well underway and one prominent feature of intensification has been the shift to fewer and larger farms. Critics suggest that increasing farm size is inimical to animal welfare because it erodes animal care values and makes it impossible to provide individual care and attention. We evaluated the empirical evidence for these claims using data from more than 100 studies, in a variety of farmed species. Research from the human organizational literature looking at relationships between organization size and various outcomes was also reviewed. Our analysis suggests that the relationship between farm size and animal welfare is far from straightforward. Although smaller farms are more likely to rear animals in more naturalistic conditions, larger farms are more likely to implement science-based, standard operating procedures, train their employees, utilize technology to track and monitor animals and implement costly changes to improve welfare. We found no evidence that farmers on large farms view animals differently, but we did find that cases of animal neglect and mistreatment are more likely to occur on smaller farms. We argue policy efforts focused on farm size are misguided; instead policy makers should try to generalize beneficial animal welfare practices independent of farm size.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".