Bibliographic record
Abstract
Meta-ethical quietists hold that only ethically-relevant considerations may bear on which ethical views to accept. Since the metaphysics of moral properties, the semantics of moral terms, and so forth, are generally not ethically relevant, they generally do not bear on whether to accept any particular ethical view, whether to drop our ethical beliefs wholesale, and so on. The quietist, then, rejects “external” or “sideways-on” vindications of ethics and ethical objectivity. In recent years, David Enoch (2011) and Tristram McPherson (2011) have offered an objection to quietist objectivism that turns this insistence on abjuring “external” vindications against the theory. They imagine alternative, “counter-normative” standards that conflict with ours—a standard of what we have “schmeason” to do, for instance, rather than what we have reason to do. To vindicate ethical objectivity, one would have to show why the “reasons”-standard is somehow privileged over the “schmeasons” standard. Enoch and McPherson argue that quietism cannot step outside the discourse of “reasons” in the way that’d be required to show this. In this paper, I explain how the quietist ought to respond to the “counternormativity” challenge.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| 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".