Sidgwick's Dualism of Practical Reason, Evolutionary Debunking, and Moral Psychology
Bibliographic record
Abstract
Abstract Sidgwick's seminal textThe Methods of Ethicsleft off with an unresolved problem that Sidgwick referred to as the dualism of practical reason. The problem is that employing Sidgwick's methodology of rational intuitionism appears to show that there are reasons to favour both egoism and utilitarianism. Katarzyna de Lazari-Radek and Peter Singer offer a solution in the form of an evolutionary debunking argument: the appeal of egoism is explainable in terms of evolutionary theory. I argue that like rational prudence, rational benevolence is subject to debunking arguments and so problematic, but also – and more importantly – that debunking arguments are irrelevant in the debate over the dualism of practical reason on the view of reason and rational intuitionism that Lazari-Radek and Singer embrace. Either both egoism and utilitarianism are debunked, or neither are. If I am right, Sidgwick's dualism is left standing.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.039 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".