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Record W2401836676 · doi:10.1177/1745691616635596

Income Mobility Breeds Tolerance for Income Inequality

2016· article· en· W2401836676 on OpenAlexaff
Azim Shariff, Dylan Wiwad, Lara B. Aknin

Bibliographic record

VenuePerspectives on Psychological Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInequalityEconomic inequalityRedistribution (election)Social mobilityIncome inequality metricsDemographic economicsPerceptionRedistribution of income and wealthAffect (linguistics)EconomicsEconomic mobilityWork (physics)Public economicsDevelopment economicsLabour economicsPolitical scienceEconomic growthPsychologyPoverty

Abstract

fetched live from OpenAlex

American politicians often justify income inequality by referencing the opportunities people have to move between economic stations. Though past research has shown associations between income mobility and resistance to wealth redistribution policies, no experimental work has tested whether perceptions of mobility influence tolerance for inequality. In this article, we present a cross-national comparison showing that income mobility is associated with tolerance for inequality and experimental work demonstrating that perceptions of higher mobility directly affect attitudes toward inequality. We find support for both the prospect of upward mobility and the view that peoples' economic station is the product of their own efforts, as mediating mechanisms.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.061
GPT teacher head0.441
Teacher spread0.380 · 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 designObservational
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

Citations205
Published2016
Admission routes1
Has abstractyes

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