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Record W3207957506 · doi:10.1080/16544951.2021.1991138

Are human rights enough? On human rights and inequality

2021· article· en· W3207957506 on OpenAlexaff
Charles W. Jones

Bibliographic record

VenueEthics & Global Politics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsWestern University
Fundersnot available
KeywordsHuman rightsInequalityLaw and economicsGlobal justiceDemocracyDistributive justiceEconomic JusticePolitical scienceSociologyNormativePoliticsLaw

Abstract

fetched live from OpenAlex

In this paper I respond to the central claims presented in Samuel Moyn’s influential book, Not Enough: Human Rights in an Unequal World. Moyn argues that human rights have the following features: they are powerless to combat growing material inequality; they share key characteristics with neoliberalism; they make only minimalist or sufficientarian demands and therefore are not enough to achieve the equality demanded by justice. He suggests, in particular, that Henry Shue’s Basic Rights exemplifies these features. My response argues that Moyn does not accurately present the core conceptual and normative characteristics of human rights, nor does he succeed in implicating Shue’s conception in his critique. I suggest that Moyn’s own ideas about global justice are incompletely developed, including his views about the scope, content, and distributive principles that should guide an account of global justice. Finally, I argue that, even though human rights are only part of an account of global justice, nonetheless they do provide reasons to limit socioeconomic inequality. This point is exemplified by the claim that a human right to democracy requires limits on material inequality in order to prevent power hierarchy. In short, I agree with Moyn that human rights are not enough by themselves to achieve global justice, but I reject his multi-pronged critique of human rights, specifically his claim that they imply no constraints on socioeconomic inequality.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.191
GPT teacher head0.457
Teacher spread0.266 · 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.

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

Citations11
Published2021
Admission routes1
Has abstractyes

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