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Record W30077582 · doi:10.1016/j.jneb.2018.06.008

The Power-Sharing Struggle in Chinese Village Elections

2014· dissertation· en· W30077582 on OpenAlexvenueno aff
Zilong Li

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Power sharingPolitical scienceGeographyPhysics

Abstract

fetched live from OpenAlex

After initiating the economic reform in 1978 and the subsequent political decentralization, Chinese Communist Party (CCP) largely lost its political control in rural areas. To regain the power, the CCP senior leaders decided to introduce the elections as a strategy to rebuild the legitimacy. After enforced all over the country, the village elections did help ameliorate the local governance and prompt the provision of public goods. However, the unclear stipulation in the Organic Law of Village Elections simultaneously resulted in some institutional outcomes, leading to a power-sharing puzzle between local party branch and the elected village committee. Using the local public policy as an indicator, I will develop a formal model in this paper to explain this puzzle and argue that when facing unpopular policies mandated from above, the struggle between local party secretary and village chief is in equilibrium. To maintain the stability in rural areas, the government leaders in different provinces have put forward various solutions to deal with the conflict, including a political experiment, which was called "Qingxian Model" later, conducted in Qing County of Hebei Province. With a field study, I argue in this paper that by resurrecting the Villager Representative Assembly, this political reform essentially reconcentrates the power to the party branch and sabotages the local democracy in the long run.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.008
GPT teacher head0.329
Teacher spread0.321 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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