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Economic Inequality and Intolerance: Attitudes toward Homosexuality in 35 Democracies

2008· article· en· W2138315171 on OpenAlexaff
Robert Andersen, Tina Fetner

Bibliographic record

VenueAmerican Journal of Political Science · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsEconomic inequalityWorld Values SurveyInequalityArgument (complex analysis)PoliticsDemocracyDevelopment economicsNormativeHomosexualitySocial inequalityState (computer science)Survey data collectionPolitical scienceEconomicsEconomic growthPolitical economyDemographic economicsSocial psychologyPsychologyMedicineLaw

Abstract

fetched live from OpenAlex

Using hierarchical linear models fitted to data from the World Values Survey and national statistics for 35 countries, this article builds on the postmaterialist thesis by assessing the impact of economic inequality across and within nations on attitudes toward homosexuality. It provides evidence that tolerance tends to decline as national income inequality rises. For professionals and managers, the results also support the postmaterialist argument that economic development leads to more tolerant attitudes. On the other hand, attitudes of the working class are generally less tolerant, and contrary to expectations of the postmaterialist thesis, are seemingly unaffected by economic development. In other words, economic development influences attitudes only for those who benefit most. These findings have political implications, suggesting that state policies that have the goal of economic growth but fail to consider economic inequality may contribute to intolerant social and political values, an attribute widely considered detrimental for the health of democracy.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.369
Teacher spread0.322 · 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

Citations376
Published2008
Admission routes1
Has abstractyes

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