Bibliographic record
Abstract
Moral error theorists face the so-called “now-what problem”: what should we do with our moral judgments from a prudential point of view if these judgments are uniformly false? On top of abolitionism and conservationism, which respectively advise us to get rid of our moral judgments and to keep them, three revisionary solutions have been proposed in the literature: expressivism (we should replace our moral judgments with conative attitudes), naturalism (we should replace our moral judgments with beliefs in non-moral facts), and fictionalism (we should replace our moral judgments with fictional attitudes). In this paper, I argue that expressivism and naturalism do not constitute genuine alternatives to abolitionism, of which they are in the end mere variants—and, even less conveniently, variants that are conform to the very spirit of abolitionism as formulated by its proponents. The main version of fictionalism, by contrast, provides us with a recommendation to which abolitionists cannot consistently subscribe. This leaves us with only one revisionary solution to the now-what problem.
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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 | 0.045 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.015 |
| 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".