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Record W2465858461 · doi:10.1177/1474885116653366

Four concepts of rules: A theory of rule egalitarianism

2016· article· en· W2465858461 on OpenAlexaff
Åsbjørn Melkevik

Bibliographic record

VenueEuropean Journal of Political Theory · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsQueen's University
Fundersnot available
KeywordsEgalitarianismPolityCapitalismClassical liberalismLaw and economicsLiberalismRule of lawSociologyStatuteEconomic JusticeLawJust societyPositive economicsEconomicsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article outlines the foundations of a nomos-observing theory of social justice, termed ‘rule egalitarianism’, that explains how the seemingly contradictory merger of classical liberalism and social justice is conceivable. The first step towards such a theory consists in ensuring that a concern for the rule of law is etched in the very core of our understanding of social justice, in which case some egalitarian rules will be acceptable from a classical liberal viewpoint. The legal framework of capitalism can indeed be designed to reduce inequality in the name of justice inasmuch as any egalitarian goal is specified in terms of institutional rules. More precisely, rules in a liberal polity should be general and abstract, which will lead us to establish a distinction between four concepts of rules, namely between laws, regulations, statutes and decrees. Moreover, against the neoclassical liberal understanding of social justice, rule egalitarianism argues that having general and abstract rules of market capitalism imposes some constraints on any institutional framework so that social justice becomes necessary to correct the errors of imperfect generalisations, leaving some people behind.

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)0.008
metaresearch head score (Gemma)0.010
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 score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0040.031
Scholarly communication0.0090.011
Open science0.0020.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.072
GPT teacher head0.343
Teacher spread0.271 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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