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Record W7117322387 · doi:10.1007/s10677-025-10512-0

Does Distributive Inequality Cause Relational Inequality? Evidence from a Survey Experiment

2025· article· en· W7117322387 on OpenAlexfundno aff
Alice Baderin, Lucy Barnes, Lindsay Richards

Bibliographic record

VenueEthical Theory and Moral Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsnot available
FundersUniversity of ReadingUniversität KonstanzUK Research and InnovationMcGill University
KeywordsNormativePolitical philosophyInequalityDistributive propertyPoliticsSurvey data collectionSocial inequalityEconomic inequalityPerceptionPriming (agriculture)

Abstract

fetched live from OpenAlex

Contemporary egalitarian theory has been shaped by a debate between distributive and relational perspectives. Relational egalitarians argue that equality is primarily about the character of our social and political relationships, rather than the pattern of distribution of goods. But they often also claim that distributive and relational ideals are connected in practice, because material inequality impairs our ability to stand as social and political equals. Using a survey experiment, we assess the impact of material inequality on relational equality. We show that priming income inequality increases perceptions of unequal social status; but it does not affect perceived equal political standing, or the extent to which respondents affirm the ideal of relational equality. We argue that this empirical approach yields deeper payoffs for conceptual and normative questions about relational equality; and it contributes to wider methodological debates about the role of survey data in normative political theory.

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)0.024
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.083
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.001

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 head0.219
GPT teacher head0.474
Teacher spread0.255 · 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 designNon-randomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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