Minds without spines: Evolutionarily inclusive animal ethics
Bibliographic record
Abstract
Invertebrate animals are frequently lumped into a single category and denied welfare protections despite their considerable cognitive, behavioral, and evolutionary diversity. Some ethical and policy inroads have been made for cephalopod molluscs and crustaceans, but the vast majority of arthropods, including the insects, remain excluded from moral consideration. We argue that this exclusion is unwarranted given the existing evidence. Anachronistic readings of evolution, which view invertebrates as lower in the scala naturae, continue to influence public policy and common morality. The assumption that small brains are unlikely to support cognition or sentience likewise persists, despite growing evidence that arthropods have converged on cognitive functions comparable to those found in vertebrates. The exclusion of invertebrates is also motivated by cognitive-affective biases that covertly influence moral judgment, as well as a flawed balancing of scientific uncertainty against moral risk. All these factors shape moral attitudes toward basal vertebrates too, but they are particularly acute in the arthropod context. Moral consistency dictates that the same standards of evidence and risk management that justify policy protections for vertebrates also support extending moral consideration to certain invertebrates. Moving beyond a vertebrate-centered conception of welfare can also clarify foundational moral concepts in their own right.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".