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Record W2769568553 · doi:10.1177/2057150x17733654

From macro-state to meso-organizing: A sociological review of the transition of Chinese local governments in the past 30 years

2017· review· en· W2769568553 on OpenAlexfundno aff
Liangfei Ye

Bibliographic record

VenueChinese Journal of Sociology · 2017
Typereview
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsPluralSociologyAgency (philosophy)Structure and agencyTransition (genetics)State (computer science)Structuralism (philosophy of science)Economic systemSociological theoryMacroPositive economicsEpistemologySocial scienceEconomics

Abstract

fetched live from OpenAlex

Studying the transition of Chinese local governments has continuously been the main theme for sociologists since the fiscal, administrative, and market reforms launched in the 1980s. In reference to other countries’ experience, sociological studies about this transition are generally from three macro-theoretical perspectives: Weberian modernity, local transitional state, and local developmental state. However, they have two common problems: first, perceiving the transition as a paradigm shift, not a process; and, second, perceiving local governments as static entities, without internal dynamics. As an alternative, the paper argues that a meso-theoretical perspective should be adopted to inductively study the organization of Chinese local governments in the transitional process. Through exploring the two schools of the meso-organizing perspective—‘structuralism’ and ‘agency with plural institutions’—the paper proposes that sociologists should explore the transition as a dynamic process, an open system, an interaction of ‘structuralism’ and ‘agency with plural institutions’, and with an internal perspective.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.010
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.379
Teacher spread0.338 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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