Understanding China’s Economic Success: “Embeddedness” with Chinese Characteristics
Bibliographic record
Abstract
This study attempts to provide a framework for understanding the role of the “embeddedness” in China’s economic success reflected by a unique embedded integration of state-market-society relations. “Embeddedness with Chinese characteristics” is the central concept of this study for analyzing how cultural and political uniqueness influences economic activities and shapes distinctive institutional forms. In order to grasp the factors behind the Chinese economic success, it is important to understand how the disembedded forces of marketization and commodification were balanced by the embedded forces of socio-cultural and political structures. These historically and culturally shaped structures, such as the active role of the state and local governments, the variety of forms of property and business ownership, the traditional culture of clientele-based social relations, etc., provide rich empirical context to explain and analyze the “embedded” hegemony in transitional China. The first part of the this paper provides a conceptual framework for understanding socio-cultural and political embeddedness in China and the second part analyzes some characteristics of the state-market-society embedded process during its economic development in the past decades. The conclusion is that China’s economic reform and success manifest a long and innovative grinding-in process of state-market-society relations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".