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Record W4297172905 · doi:10.1111/theo.12433

Distributive justice, social cooperation, and the basis of equality

2022· article· en· W4297172905 on OpenAlexaff
Emil Andersson

Bibliographic record

VenueTheoria · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsMcGill University
FundersStockholms UniversitetUppsala UniversitetRiksbankens Jubileumsfond
KeywordsDistributive justiceDistributive propertyLaw and economicsEconomic JusticeBasis (linear algebra)Social justiceSociologyPolitical scienceSocial psychologyPsychologyLawMathematicsPure mathematics

Abstract

fetched live from OpenAlex

Abstract This paper considers the view that the basis of equality is the range property of being a moral person. This view, suggested by John Rawls in his A Theory of Justice (1971), is commonly dismissed in the literature. By defending the view against the criticism levelled against it, I aim to show that this dismissal has been too quick. The critics have generally failed to fully appreciate the fact that Rawls's account is restricted to the domain of distributive justice. On Rawls's view distributive justice is a matter of the fair terms of cooperation among the participants of a system of social cooperation. I argue that this understanding of distributive justice can provide a compelling rationale for considering moral personality as the basis of equality for this domain of morality. That moral persons are indeed equal is further supported by an intuitive argument concerning the irrelevance of morally arbitrary factors, giving us reasons to believe that varying capacities among moral persons does not result in an unequal moral status. The dismissal of Rawls's account of equality has thus been premature, and it remains an important view to consider.

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)0.008
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 score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.035
Scholarly communication0.0060.006
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.050
GPT teacher head0.361
Teacher spread0.311 · 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
Published2022
Admission routes1
Has abstractyes

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