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Record W1750196828 · doi:10.1111/phpr.12187

Intuitions, Meaning, and Normativity: Why Intuition Theory Supports a Non‐Descriptivist Metaethic

2015· article· en· W1750196828 on OpenAlexafffund
Matthew S. Bedke

Bibliographic record

VenuePhilosophy and Phenomenological Research · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntuitionExpressivismNormativeEpistemologyCategorizationLinguistic descriptionPsychologyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.416
GPT teacher head0.376
Teacher spread0.040 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2015
Admission routes2
Has abstractyes

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