Choosing normative properties: a reply to Eklund’s <i>Choosing Normative Concepts</i>
Bibliographic record
Abstract
The literature surrounding Horgan and Timmons’s Moral Twin Earth scenarios has focused on whether such scenarios present a metasemantic problem for naturalist realists. But in Choosing Normative Concepts, Eklund uses a similar scenario to illuminate a novel, distinctly metaphysical problem for normative realists of both naturalist and non-naturalist stripes. The problem is that it is not clear what (if anything) would suffice for the sort of ardent realist view that normative realists have in mind – the view that reality itself favors certain ways of acting and valuing. Eklund then offers a metasemantic view that he thinks can provide the best solution to this problem. In this reply to Eklund, I argue that Eklund’s treatment of the problem and his solution re-entangle metaphysical and metasemantic issues that ought to be kept separate. I also argue that there is a purely metaphysical solution to the problem at hand, which Eklund’s own solution seems to implicitly rely upon. While these criticisms do not suggest that Eklund’s positive view is false, they do undermine some of the broader lessons that Eklund hopes to draw from the view.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".