Bibliographic record
Abstract
Abstract Arguably, grounding animal ethics in traditional moral theories such as utilitarianism or rights-based ethics is impoverished since they emphasise impartiality and abstractness in our ethical deliberations at the expense of giving proper weight to special relationships we have with other individuals. Here, I explore the human-animal bond as a starting point for animal ethics, and focus on the resulting moral implications of this bond on farm animal welfare. The human-animal bond revisits values inherent in the nature of animal husbandry and is also influenced by philosophical ethics of caring. Farmers or stockpersons who form close bonds with their animals make an implicit promise to discharge duties to their animal companions above and beyond respectful treatment as sentient beings. Scientific study suggests that interpersonal human-animal relationships may translate to better care and consideration for farmed animals, promoting both better animal welfare and on-farm productivity. Acknowledging the existence of human-animal bonds on the farm and encouraging farmers and animal handlers not to shy away from forming bonds with their animals is recommended. Farmers, stockpersons, and contract-farmers for agribusinesses should be given an ethical voice to lodge grievances about how farmed animals are treated and be encouraged to participate in discussions on farming practices and animal welfare standards. They should also be educated on gains made through scientific enquiry regarding the capacities and needs of animals as well as on welfare advances.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".