WHY ANIMAL WELFARE IS NOT BIODIVERSITY, ECOSYSTEM SERVICES, OR HUMAN WELFARE: TOWARD A MORE COMPLETE ASSESSMENT OF CLIMATE IMPACTS
Bibliographic record
Abstract
Taking the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) as representative, I argue that animal ethics has been neglected in the assessment of climate policy. While effects on ecosystem services, biodiversity, and human welfare are all catalogued quite carefully, there is no consideration at all of the effects of climate change on the welfare of animals. This omission, I argue, should bother us, for animal welfare is not adequately captured by assessments of ecosystem services, biodiversity, or human welfare. After describing the paper’s assumptions and discussing the role of the IPCC’s Assessment Reports in climate policy, I consider the presentation of climate impacts in the IPCC’s Fifth Assessment Report, noting the aspects of animal welfare that are (and are not) considered there, and comparing the report’s treatment of animal welfare to its treatment of human welfare. Next, I argue that the concepts of ecosystem services, biodiversity, and human welfare do not adequately capture the welfare of animals. Finally, I discuss concerns about human responsibility for animal welfare and the practicality of including considerations of animal welfare among the climate impacts studied by the IPCC.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".