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Record W2769330750 · doi:10.1080/0020174x.2017.1402699

Questions open and closed: lessons from metaethics for identity arguments for the existence of god

2017· article· en· W2769330750 on OpenAlexaff
Andrew Sneddon

Bibliographic record

VenueInquiry · 2017
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIdentity (music)EpistemologyNaturalismExistentialismPhilosophyArgument (complex analysis)Perspective (graphical)SociologyAestheticsMathematics

Abstract

fetched live from OpenAlex

Identity arguments for the existence of god offer an intriguing blend of conceptual and existential claims. As it happens, this sort of blend has been probed for more than a century in metaethics, ever since G.E. Moore formulated the Open Question Argument (OQA) against metaethical naturalism. Moore envisaged naturalism as offering identity claims between good and natural properties. His central worry was that such identity claims should render certain questions closed and hence meaningless. However, he contended that speakers competent with the respective concepts would find these questions meaningful, thereby indicating the failure of the identity claim in question, and of all such identity claims. The history of this issue in metaethics provides an important perspective on the prospects of devising cogent, rhetorically successful versions of identity arguments for the existence of god. Both the conceptual and existential premises of such arguments involve conceptual nuances that metaethicists have studied. Overall, the history of the OQA provides reason to think that rationally compelling versions of identity arguments for the existence of god are very unlikely to be constructed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0060.083
Scholarly communication0.0140.032
Open science0.0040.009
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.586
GPT teacher head0.493
Teacher spread0.092 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes1
Has abstractyes

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