‘More than a feeling’: An empirical investigation of hedonistic accounts of animal welfare
Bibliographic record
Abstract
Many scientists studying animal welfare appear to hold a hedonistic concept of welfare -whereby welfare is ultimately reducible to an animal's subjective experience. The substantial advances in assessing animal's subjective experience have enabled us to take a step back to consider whether such indicators are all one needs to know if one is interested in the welfare of an individual. To investigate this claim, we randomly assigned participants (n = 502) to read one of four vignettes describing a hypothetical chimpanzee and asked them to make judgments about the animal's welfare. Vignettes were designed to systematically manipulate the descriptive mental states the chimpanzee was described as experiencing: feels good (FG) vs. feels bad (FB); as well as non-subjective features of the animal's life: natural living and physical healthy (NH) vs. unnatural life and physically unhealthy (UU); creating a fully-crossed 2 (subjective experience) X 2 (objective life value) experimental design. Multiple regression analysis showed welfare judgments depended on the objective features of the animal's life more than they did on how the animal was feeling: a chimpanzee living a natural life with negative emotions was rated as having better welfare than a chimpanzee living an unnatural life with positive emotions. We also found that the supposedly more purely psychological concept of happiness was also influenced by normative judgments about the animal's life. For chimpanzees with positive emotions, those living a more natural life were rated as happier than those living an unnatural life. Insofar as analyses of animal welfare are assumed to be reflective of folk intuitions, these findings raise questions about a strict hedonistic account of animal welfare. More generally, this research demonstrates the potential utility of using empirical methods to address conceptual problems in animal welfare and ethics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".