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Record W95209648 · doi:10.7202/1044697ar

On the Scope of Egalitarian Justice

2018· article· en· W95209648 on OpenAlexaffvenue
Joseph Heath

Bibliographic record

VenueLes ateliers de l éthique · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRedressEgalitarianismObligationLaw and economicsScope (computer science)Social contractNoticeNatural (archaeology)Economic JusticeEconomicsInequalityRedistribution (election)SociologyPolitical scienceLawPositive economicsPoliticsMathematics

Abstract

fetched live from OpenAlex

It is not clear whether the social contract is supposed to merely supplement the unequal gains that individuals are able to make through the exercise of their natural endowments with a set of equal gains secured through social cooperation, or whether the social contract must also compensate individuals for the effects of these natural inequalities, so that they literally become all equal. The issue concerns, in effect, whether natural inequality falls within the scope of egalitarian justice. I think it is fair to say that the majority of egalitarians assume that the principle of equality imposes an obligation to redress natural inequality. Yet there is no consensus on this issue. David Gauthier has made the rejection of the principle of redress a central component of his project. It has often escaped notice that John Rawls also rejects the principle of redress. Thus it is not just anti-egalitarians who reject the principle of redress. There is a current of egalitarian thought – which we might call, for lack of a better term, narrow-scope egalitarianism – which also rejects this principle. In this paper, I would like to show that there is considerable wisdom in the narrow-scope egalitarian position. Many of the problems that lead theorists to reject egalitarianism in its entirety are a consequence, not of the principle of equality per se, but rather of the commitment to redress natural inequality. The narrow-scope view avoids all of these difficulties.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.937
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.356
Teacher spread0.287 · 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.

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

Citations7
Published2018
Admission routes2
Has abstractyes

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