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Record W3011089423 · doi:10.1177/0738894220906374

Public opinion, international reputation, and audience costs in an authoritarian regime

2020· article· en· W3011089423 on OpenAlexaff
Xiaojun Li, Dingding Chen

Bibliographic record

VenueConflict Management and Peace Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAuthoritarianismReputationPublic opinionScholarshipChinaPolitical scienceGovernment (linguistics)Public relationsState (computer science)Political economyPublic administrationSociologyDemocracyLawPolitics

Abstract

fetched live from OpenAlex

Does the public in authoritarian regimes disapprove of their leaders’ backing down from public threats and commitments? Answers to this question provide a critical micro-foundation for the emerging scholarship on authoritarian audience costs. We investigate this question by implementing a series of survey experiments in China, a single-party authoritarian state. Findings based on responses from 5375 Chinese adults show that empty threats and commitments expose the Chinese government to substantial disapproval from citizens concerned about potential damage to China’s international reputation. Additional qualitative evidence reveals that Chinese citizens are willing to express their discontent of leaders’ foreign policy blunders through various channels. These findings contribute to the ongoing debate over whether and how domestic audiences can make commitments credible in authoritarian states.

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)0.005
metaresearch head score (Gemma)0.019
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 score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.108
GPT teacher head0.362
Teacher spread0.254 · 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

Citations70
Published2020
Admission routes1
Has abstractyes

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