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Record W4413741140 · doi:10.1017/psrm.2025.10037

On the political consequences of local deliberative governance in China

2025· article· en· W4413741140 on OpenAlexaff
Jidong Chen, Yukun Wang, Ming‐ang Zhang

Bibliographic record

VenuePolitical Science Research and Methods · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsInstitute on Governance
FundersUniversity of CambridgeUniversity of RochesterAmerican Political Science Association
KeywordsChinaPoliticsCorporate governancePolitical sciencePolitical economyEconomic systemEconomicsManagementLaw

Abstract

fetched live from OpenAlex

Abstract How can local governments in developing countries, constrained by limited resources, identify and respond to the most pressing public demands? This paper posits that public deliberative platforms, even those with controlled agendas, can be instrumental in this regard by facilitating communication between local elites and ordinary citizens, thereby leading to an observable uptick in political trust over time. Public deliberation serves two functions: firstly, it highlights shifting societal issues, incentivizing bureaucrats to respond more promptly; and secondly, it generates narratives that temporarily improve the public perception of local governments, even among individuals not directly benefiting from government actions. This study provides evidence consistent with these theoretical implications by examining Chinese topical debate programs, during which local officials engage with citizens and respond to their concerns. Empirical results based on a staggered difference-in-differences design suggest that broadcasting such programs in China produces a prompt increase in citizens’ trust in local officials. Our results demonstrate that public deliberation can yield noticeable outcomes in developing countries, even with controlled agendas and constrained resources.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.071
GPT teacher head0.519
Teacher spread0.448 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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