Bibliographic record
Abstract
Abstract Observations of animals engaging in apparently moral behaviour have prompted the question of whether morality is shared between humans and other animals, with little agreement on the answer. Some philosophers explicitly argue that morality is unique to humans, because moral agency requires capacities that are only demonstrated in our species. Other philosophers argue that some animals can participate in morality because they possess these capacities in a rudimentary form, or because the touted capacities are not necessary for moral participation. Empirical research programs on possible moral capacities such as fairness and empathy have seen scientists joining in these debates. We argue that the current debate suffers because discussions often fail to provide both a proper philosophical foundation about the nature of moral practices and a solid empirical ground for claims about what animals can and cannot do. In this chapter we focus on the second of these issues, and defend the claim that animals have three sets of capacities that, on some views, are taken as necessary or foundational for moral judgment and action. These are capacities of care, capacities of autonomy, and normative capacities. Care, we argue, is widely found among social animals. Autonomy and normativity are more recent topics of empirical investigation, so while there is less evidence of these capacities at this point in our developing scientific knowledge, the current data is strongly suggestive.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".