Bibliographic record
Abstract
Sidgwick gives various tests for highest certainty. When he applies these tests to commonsense morality, he finds nothing of highest certainty. In contrast, when he applies these tests to his own axioms, he finds these axioms to have highest certainty. The axioms culminate in Benevolence: “Each one is morally bound to regard the good of any other individual as much as his own, except in so far as he judges it to be less, when impartially viewed, or less certainly knowable or attainable by him.” The axioms face challenges from two sides. First, one test requires that a claim not be denied by someone of whom one has no more reason to suspect of error than oneself. For Sidgwick, then, the egoist must not deny the axioms. But it would seem that an egoist would reject benevolence. Second, Sidgwick thinks he must show that the commonsense moralist agrees to the axioms. Benevolence seems to say that the only reason for departing from being bound to treat others like oneself is that more good would be produced. But the commonsense moralist will not agree that this is the only reason. In reply to the threat of an egoist's disagreement, this essay argues that many of the axioms should be read as having as their antecedent “from the point of view of the universe.” The essay replies to the objection that this makes these axioms analytic. In reply to the threat of a commonsense moralist's disagreement, this essay argues that each axiom states, in effect, a prima facie duty. The argument against the commonsense moralist concerns not benevolence but whether there are further duties that pass the tests. The essay raises the worry that here Sidgwick is unfair since sometimes he criticizes all-things-considered versions of commonsense duties; such criticisms would count against benevolence as well.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".