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Record W1484025478 · doi:10.7202/1044464ar

Perfectionism, Economic (Dis)Incentives, and Political Coercion

2018· article· en· W1484025478 on OpenAlexvenueno aff
Oran Moked

Bibliographic record

VenueLes ateliers de l éthique · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsCoercion (linguistics)Perfectionism (psychology)PoliticsGovernment (linguistics)IncentiveSubject (documents)Action (physics)Value (mathematics)Economic JusticePolitical scienceSocial psychologySociologyLaw and economicsPolitical economyLawPsychologyEconomics

Abstract

fetched live from OpenAlex

May a government attempt to improve the lives of its citizens by promoting the activities it deems valuable and discouraging those it disvalues? May it engage in such a practice even when doing so is not a requirement of justice in some strict sense, and even when the judgments of value and disvalue in question are likely to be subject to controversy among its citizens? These questions have long stood at the center of debates between political perfectionists and political neutralists. In what follows I address a prominent cluster of arguments against political perfectionism—namely, arguments that focus on the coercive dimensions of state action. My main claim is simple: whatever concerns we might have about coercion, arguments from coercion fall short of supporting a thoroughgoing rejection of perfectionism, for the reason that perfectionist policies need not be coercive. The main body of the paper responds, however, to several neutralist challenges to this last claim.

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)0.009
metaresearch head score (Gemma)0.013
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 score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.037
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.332
Teacher spread0.301 · 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
Published2018
Admission routes1
Has abstractyes

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