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Record W1966418997 · doi:10.1111/twec.12193

The China Growth Miracle: The Role of the Formal and the Informal Institutions

2014· article· en· W1966418997 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Economy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInstitutionChinaCorporate governanceEconomicsMiraclePanel dataQuality (philosophy)Index (typography)EstimatorLocal governanceSurvey data collectionFixed effects modelEconometricsPolitical scienceSociologyFinanceSocial scienceStatisticsLawMathematics

Abstract

fetched live from OpenAlex

Abstract This paper examines why C hina, in spite of its ordinary institutions, can grow so rapidly and for so long. Since each region in China has different quality of institutions and growth rates, we look into provincial and city data for this investigation. The variables formal and informal institutions are added into the conventional cross‐section growth equation. The quality of the formal (informal) institution is taken from an opinion survey on the effectiveness of city governance conducted by the W orld B ank in 2006 (can be measured by the share of township‐and‐village enterprise in each province during 1978–2002 or by the trust index from surveys). We conclude that it is the informal institution that drives the rapid growth in China. Further investigation, using panel data and A rellano‐ B ond system GMM estimator, which controls for the missing fixed effect in cross‐provincial regressions and provides useful instrument, confirms.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.213
Teacher spread0.209 · 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