Expanding perspectives and understanding relational potential: Are mutually beneficial human-animal relationships compatible with current animal agricultural practices?
Bibliographic record
Abstract
Animal agriculture employs approximately one-eighth of world's human population and results in the slaughter of over 160 billion animals annually, representing perhaps the most extensive intertwining of human and animal lives on the planet. In principle, close, intersubjective relationships (involving shared attention and mental states) between humans and the animals in agriculture are possible, though these are infrequently studied and are unlikely to be achieved in farming, given systemic constraints (e.g. housing and management). Much scientific research on human-animal relationships within agriculture has focused upon a fairly restricted range of states (e.g. reducing aversive human-animal interactions within standard systems, toward improving productivity and reducing injuries to workers). Considering human-animal relations along a continuum, we review scholarship supporting the rationale for expanding the range of relationships under consideration in animal welfare research, given the impacts these relationships can have on both animals and stockpersons, increasing consumer demand for humane food products, and the goal of providing animals under our care with good lives. Looking toward traditions that encourage taking the perspective of, and learning from non-humans, we provide entry points to approaches that can enable animal welfare research to expand to investigate a broader range of human-animal relationship states. By showing the potential for close mutually beneficial human-animal relationships, this line of research highlights pathways for understanding and improving the welfare of animals used in agriculture.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".