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Record W4388596552 · doi:10.1177/00207152231211483

How anti-corruption actions win hearts: The evaluation of anti-corruption performance, social inequality and political trust—Evidence from the Asian Barometer Survey and the Latino Barometer Survey

2023· article· en· W4388596552 on OpenAlexvenueno aff
Lei Yue, 倩 刘

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
FundersNational Office for Philosophy and Social SciencesZhengzhou University
KeywordsBarometerLanguage changeEuropean Social SurveyPoliticsPolitical corruptionInequalityGeneral Social SurveyPolitical scienceDevelopment economicsPolitical economyEconomicsSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

Corruption is seen as a political cancer that erodes the public’s trust in political system. While it is generally agreed that corruption and political trust are negatively correlated, few researchers have explored the impact of anti-corruption performance on political trust. In this article, we focus on whether combating corruption can enhance political trust and how this influencing mechanism is realized. Based on analyses of the Asian Barometer Survey and the Latino Barometer Survey, we found that political trust is affected by the evaluation of anti-corruption performance and social inequality. The evaluation of anti-corruption performance can enhance political trust directly, while social inequality undermines political trust directly. Social inequality can also moderate the positive effect of the evaluation of anti-corruption performance on political trust. This study not only fills the previous research gap in the relationship between anti-corruption and political trust but also has great practical significance.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.043
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.320
GPT teacher head0.449
Teacher spread0.129 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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