Review: Using animal welfare to frame discussion on dairy farm technology
Bibliographic record
Abstract
The use of technology on dairy farms has increased dramatically over the last half-century. The ways that scientists describe the potential benefits and risk of technology are likely to affect if it is accepted for use on farms. The aim of our study was to identify papers that describe a linkage between technologies used on dairy farms and the welfare of dairy cattle. Our systematic review identified 380 papers, of which 28 met our inclusion criteria and were used to describe the technologies examined, the welfare-relevant measures used, and the ways in which authors framed welfare benefits and risks associated with the technologies. The large majority (27 of 28 papers) used positive frames, considering how the technology could improve welfare. Some authors carefully articulated the logic linking the specific measures to specific welfare-related outcomes (such as the use of accelerometer data to draw inferences about changes in lying times), but others made more general inferences (about health and welfare) that were not (and perhaps could not) be assessed. We conclude that much of the framing focused on animal welfare is biased toward welfare benefits and that future work should strive to address both potential benefits and harms; more balanced coverage may better inform solutions to the problems encountered by the people and animals affected by the technology. Welfare is a complex and multifaced concept, and it is unlikely that any one technology (or perhaps even a combination of technologies) can adequately capture this complexity. Thus, we encourage authors to restrict their claims to specific, welfare-relevant measures that can be assessed independently and thus validated. More general claims about welfare should be treated with skepticism.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".