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Record W4405591645 · doi:10.1017/jdm.2024.38

Westerners underestimate global inequality

2024· article· en· W4405591645 on OpenAlexaff
Ignazio Ziano, Ivuoma N. Onyeador, Nandita Dhanda

Bibliographic record

VenueJudgment and Decision Making · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsInequalityPsychologyEconometricsEconomicsMathematics

Abstract

fetched live from OpenAlex

Abstract Most global inequality is between countries, but inequality perceptions have mostly been investigated within the country. Six studies (total N = 2656, 5 preregistered, 1 incentivized for accuracy, 1 with a sample representative of the USA) show that Westerners (U.S. American, British, and French participants) believe that developing and middle-income countries’ GDP per capita is much closer to developed countries’ than it actually is, and that people in developing and middle-income countries have higher rates of car ownership, larger houses, and eat out more frequently than they actually do, meaning that Westerners underestimate global inequality. This misperception is underpinned by a convergence illusion: the belief that over time, poorer countries have closed the economic gap with richer countries to a larger extent than they have. Further, overestimating GDP per capita is negatively correlated with support for aid to the target country and positively correlated with a country’s perceived military threat. We discuss implications for inequality perceptions and for global economic justice.

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)0.004
metaresearch head score (Gemma)0.014
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 score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.077
GPT teacher head0.407
Teacher spread0.330 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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