Intuitions, Meaning, and Normativity: Why Intuition Theory Supports a Non‐Descriptivist Metaethic
Bibliographic record
Abstract
Non‐descriptivists in metaethics should say more about intuitions. For one popular theory has it that case‐based intuitions are in the business of correctly categorizing or classifying (as not water, as not knowledge, as impermissible, etc.) merely by bringing to bear a semantic or conceptual competence. If so, then the fact that all normative predicates have case‐based intuitions involving them shows that they too are in the business of categorizing or classifying things. This favors a descriptivist position in metaethics—normative predicates have descriptive content—and disfavors a purely non‐descriptivist position, like pure expressivism. However, we can say more. We can distinguish two different sorts of intuitional state, A‐grade intuitions and B‐grade intuitions, based on a cluster of properties that are distinctive of each. While a hypothesis about categorization best explains the cluster of properties enjoyed by A‐grade intuitions, it does not best explain the cluster of properties enjoyed by B‐grade intuitions. Indeed, a non‐categorizational (and so non‐descriptive), attitude‐expressive hypothesis about the relevant meanings best explains B‐grade intuitions. And intuitions involving thin normative predicates are B‐grade. So intuition theory supports non‐descriptivism, not descriptivism, about thin normative predicates.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| 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".