IS DANIEL A MONSTER? REFLECTIONS ON DANIEL A. BELL AND WANG PEI’S “SUBORDINATION WITHOUT CRUELTY” THESIS
Bibliographic record
Abstract
Daniel Bell and Wang Pei’s recent monograph, Just Hierarchy, seeks to defend hierarchical relationships against more egalitarian alternatives. This paper addresses their argument, offered in one chapter of the book, in favour of a hierarchical relationship between human and nonhuman animals. This relationship, Bell and Pei argue, should conform to what they call “subordination without cruelty:” it is permissible to subordinate and exploit animals for human ends, provided that we do not treat them cruelly. We focus on three aspects of their view: their argument for a hierarchical view; their understanding of cruelty; and their account of the heightened duties they claim we owe to nonhuman animals who are intelligent, domesticated, and/or “cute.” We argue that the reasons that Bell and Pei offer fail to support their conclusions, and that, even if one accepts a hierarchical view, the conclusions that Bell and Pei draw about the permissibility of practices such as killing animals for food do not follow. We conclude by emphasizing philosophers’ responsibility to thoroughly test their arguments and to engage with existing debates, especially when the practices they seek to justify involve harms of great magnitude.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".