From Moral Supervenience to Moral Contingentism (In One Easy Step!)
Bibliographic record
Abstract
ABSTRACT According to the Divide & Conquer (DC) strategy (Fogal and Risberg 2020) for explaining moral supervenience, the modal covariation between moral and natural properties can be partly explained by appeal to pure moral principles. Bhogal (2022) has recently argued that DC fails. A pure moral principle like Act Utilitarianism (AU) cannot explain moral supervenience because AU is not a difference‐maker for moral supervenience. There is nothing special about AU which explains why moral properties supervene on natural properties; other moral principles would also explain moral supervenience. On the other hand, if the proponent of DC appeals to some general feature of pure moral principles (like the fact that they have a “bridge‐law” structure) then there is a question of what explains that feature. In this paper I do two things. First, I explore a possible extension of the DC‐strategy against Bhogal's objection: I consider whether pure moral principles have the right features to explain moral supervenience by showing how these features follow from plausible assumptions about the underlying metaphysics of moral principles. Secondly, I show how this extended version of the DC‐strategy can be used as a novel argument for moral contingentism.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".