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Record W3035806552 · doi:10.1177/0191453720931904

Good life egalitarianism

2020· article· en· W3035806552 on OpenAlexaff
Tom Malleson

Bibliographic record

VenuePhilosophy & Social Criticism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsEgalitarianismLuckPossession (linguistics)The good lifeSociologyInequalityArbitrarinessPositive economicsLaw and economicsSocial psychologyEpistemologyEconomicsLawPsychologyPhilosophyPolitical scienceMathematicsPolitics

Abstract

fetched live from OpenAlex

This article carves out a new path between the two dominant wings of contemporary egalitarianism. The luck egalitarian emphasis on choice and personal responsibility is misplaced because individuals differ so deeply, and arbitrarily, in their choice-making capacities. Allowing inequalities to result from ‘choice’ is akin to allowing inequalities to stem from the possession of any other morally arbitrary factor – such as skin colour or gender. The move towards relational egalitarianism has been a case of two-steps forward, one-step back. While the shift away from the focus on choice is salutary, the concurrent rejection of luck is problematic, given the prevalence and importance of luck-based discrepancies in opportunities to lead a good life. A new conceptual framework is presented: good life egalitarianism. The guiding idea is that given the unavoidable arbitrariness of human capacities, the foundation for a good life should be assured for people regardless of the actual choices that they make. The essential goods necessary for leading a good life – such as the opportunities to self-determine and to enjoy non-dominating social relationships – should be guaranteed to all.

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)0.007
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
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.046
Scholarly communication0.0060.006
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.143
GPT teacher head0.361
Teacher spread0.219 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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