Bibliographic record
Abstract
InThe Methods of EthicsHenry Sidgwick argued against deontology and for consequentialism. More specifically, he stated four conditions for self-evident moral truth and argued that, whereas no deontological principles satisfy all four conditions, the principles that generate consequentialism do. This article argues that both his critique of deontology and his defence of consequentialism fail, largely for the same reason: that he did not clearly grasp the concept W. D. Ross later introduced of a prima facie duty or duty other things equal. The moderate deontology Ross's concept allows avoids many of Sidgwick's objections. And Sidgwick's statements of his own axioms equivocate in exactly the same way for which he criticized deontological ones. Only if they are read as other things equal can they seem intuitive and earn widespread agreement; but that form is too weak to ground consequentialism. And in the form that does yield consequentialism they are neither intuitive nor widely accepted. Sidgwick's arguments against a rival view and for his own were, in multiple ways, unfair.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| 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".