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Record W2806168640 · doi:10.3138/cpp.2017-014

Preferences for the Distribution of Incomes in Modern Societies: The Enduring Influence of Social Class and Economic Context

2018· article· en· W2806168640 on OpenAlexaffvenueabout
Robert Andersen, Meir Yaish

Bibliographic record

VenueCanadian Public Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsProsperitySocial classInequalityContext (archaeology)Distribution (mathematics)Economic inequalitySocial inequalityLife chancesIncome distributionDemographic economicsSocioeconomic statusSocial stratificationSocial mobilitySociologyEconomicsSocial psychologyPolitical scienceEconomic growthPsychologySocial scienceGeographyDemographyPopulationLaw

Abstract

fetched live from OpenAlex

Using International Social Survey Program data, we explore the relationship between economic context and attitudes with respect to the distribution of incomes in 20 modern societies, including Canada. Our findings demonstrate that economic inequality has an enduring influence on attitudes. Consistent with the economic self-interest thesis, preferences for equality are strongest among those in working-class occupations. Moreover, independent of one's own social class, one's father's social class has a similar enduring impact on attitudes later in life. These relationships are relatively similar across the 20 societies we explore. Still, significant differences in attitudes can be explained by national economic context. We find a strong positive relationship between national-level inequality and opinions on how much inequality there ought to be in the income distribution. In contrast to previous research, however, our findings suggest that national-level economic prosperity and equality of opportunity have little influence on public opinion.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.341
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2018
Admission routes3
Has abstractyes

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