Dreier Is a Great Dad in All Possible Worlds: A Challenge to Moral Contingentism
Bibliographic record
Abstract
In this paper, I raise a challenge to Gideon Rosen’s defence of moral contingentism against Jamie Dreier’s moral luck argument. Dreier argues that if moral contingentism is true, acting in a morally permissible way always depends on a form of moral luck, because we could be in a descriptively identical possible world where the moral laws are different. Rosen’s response is that such a world is too remote from ours for us to count it as lucky that we are not in it. I argue that, given Rosen’s method of assessing the remoteness of possible worlds, worlds like the one Dreier describes are close enough to ours to justify his worry, and consequently that Rosen’s counterargument fails. I take this strongly counterintuitive conclusion as a reason to be optimistic that Rosen’s argument for moral contingentism can be resisted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".