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Record W2770713964 · doi:10.1080/13698230.2017.1398868

Engaging the reluctant taxpayer

2017· article· en· W2770713964 on OpenAlexaff
Michael Joel Kessler

Bibliographic record

VenueCritical Review of International Social and Political Philosophy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsTrinity CollegeUniversity of Toronto
FundersNational Endowment for the Arts
KeywordsThe artsArgument (complex analysis)NeutralityPoliticsLaw and economicsSociologyValue (mathematics)LawPublic valueComplaintCitizenshipPolitical scienceComputer science

Abstract

fetched live from OpenAlex

A commitment to political neutrality means that citizens have a legitimate complaint when the coercive power of the state is used to advance some particular conception of how it is good to live. In this paper I investigate how to address this complaint in the case of public funding for the arts. There are two promising ways to justify public arts spending. First, as Thomas Nagel argues, the arts are a source of intrinsic values and so command our respect. I reject this argument because intrinsic values are not automatically political values. Second, Ronald Dworkin argues that access to the arts is required to fully participate in social life. This argument draws a connection between the arts and citizenship and so fares better in establishing a political justification for the arts. However, Dworkin relies on the special value of high art relative to popular art, which undermines the neutrality of his argument. I show that a justification can be given that does not depend on the high value of the arts. I develop an account that shows how the arts can support just relations between citizens. This account is in keeping with a liberal commitment to neutrality.

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)0.020
metaresearch head score (Gemma)0.039
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 score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.014
Scholarly communication0.0110.008
Open science0.0020.006
Research integrity0.0160.016
Insufficient payload (model declined to judge)0.0110.003

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 head0.147
GPT teacher head0.446
Teacher spread0.299 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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