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Record W3126458004 · doi:10.26443/firr.v11i1.54

Two Birds, One Policy: The Establishment of the National Supervisory Commission as a Factional and Centralizing Tool

2021· article· en· W3126458004 on OpenAlexvenueno aff
Maya Mainland-Gratton, Naomi Shi, Liam Olsen

Bibliographic record

VenueFlux International Relations Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsLanguage changeCommissionCredibilityAccountabilityPower (physics)PoliticsPublic administrationChinaGovernment (linguistics)RestructuringPopulationPolitical scienceLocal governmentLawSociology

Abstract

fetched live from OpenAlex

Xi Jinping has made “anti-corruption” campaigns a hallmark of his leadership. The campaigns promise to target both “tigers” - senior party, government, and military leaders - and “flies” - local party and government officials. This practice has included a drastic restructuring of China’s anti-corruption and judicial agencies, culminating in their centralization under the National Supervisory Commission (NSC) in 2018. Many scholars have debated whether Xi’s campaigns and the NSC are genuinely intended to combat corruption or are instead a tool to eliminate political opponents and consolidate power. The NSC’s establishment is considered in relation to the two predominant models of anti-corruption drives conducted in China, the “Chongqing” model, and the “Guangzhou” model. By deliberately reproducing the Chongqing model’s accountability defects, eliminating political opponents appears to be a core objective of the NSC’s establishment. However, owing to its centralized nature, the NSC also strengthens the central party’s power over local authorities. Local party branches are far less trusted by the population than their national counterparts. Thus, strengthening the party’s credibility - including a genuine attempt to decrease corruption - and strengthening local government oversight appears to be another objective of the NSC’s establishment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.046
GPT teacher head0.348
Teacher spread0.302 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2021
Admission routes1
Has abstractyes

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