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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".