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Who Wants to Redistribute? An Analysis of 14 Post‐Soviet Nations

2011· article· en· W1546332539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Policy and Administration · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRedistribution (election)InequalityWelfare stateIdeologyPopulationDevelopment economicsDemographic economicsWelfareIndividualismPolitical scienceEconomicsPolitical economyEconomic systemMarket economySociologyPoliticsDemography

Abstract

fetched live from OpenAlex

Abstract We investigate population groups' attitude regarding inequality reduction in post‐Soviet transitional countries of the Baltic, Central Asia and the Caucasus, as well as the Slavic countries and Moldova. Empirical evidence presented in this article demonstrates that despite skyrocketing inequality, erosion of social provisions and efforts to introduce an individualistic market economy ideology during the last 15 years, overall support for redistribution and welfare state efforts to counterbalance rising inequality remained strongly legitimized among citizens in all post‐Soviet countries. Nevertheless, there are differences between population groups in attitude: the older, the less educated, the poor and women express more support for redistribution; while the younger, the better educated, the rich and men tend to not support redistribution. Populations in transitional countries of the Caucasus and Central Asia that face higher inequality and less effective redistribution policies expressed a strong desire for more redistribution and more active social welfare policies.

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.

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 categoriesScience and technology studies
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 score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.070
GPT teacher head0.398
Teacher spread0.328 · 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