Bibliographic record
Abstract
Moral Uncertainty and the Principle of Equity among Moral Theories Andrew Sepielli Rutgers University – New Brunswick Department of Philosophy Abstract Suppose your credence is divided between two moral theories – Theory T and Theory U. According to T, you have more reason to do Action A than you have to do Action B. According to U, you have more reason to do B than you have to do A. What is it rational to do in a situation in which A and B are the two possible actions? Many have argued that what it’s rational to do depends on two things: (a) how your credence is distributed between the theories, and (b) how the difference in moral value between A and B if T is true compares to the difference in moral value between B and A if U is true. But this answer prompts a further question: How do we make the intertheoretic comparisons of value differences mentioned in (b)? The theories themselves seem not to provide the resources required to do so. In Moral Uncertainty and Its Consequences, Ted Lockhart argues that intertheoretic comparisons of value differences are possible if we adopt a principle he calls the “Principle of Equity among Moral Theories”. I argue on several grounds that this principle is untenable, consider some rejoinders on Lockhart’s behalf, and conclude that these rejoinders do not succeed. Suppose your credence is divided between two moral theories – Theory T and Theory U. According to T, you have more reason to do Action A than you have to do Action B. According to U, you have more reason to do B than you have to do A. Many real-life cases fall under this schema. For example, I might have some credence in a retributive theory of punishment and some credence in a non- retributive theory of punishment. According to the first theory, it may be better to subject a criminal to very harsh treatment than to rehabilitate him. According to the second theory, the reverse may be true. Or I might have some credence in a traditional consequentialist theory, and some credence in a non-consequentialist
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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 | 0.034 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".