Review of “Markets with Bureaucratic Characteristics: How Economic Bureaucrats Make Policies and Remake the Chinese State”
Bibliographic record
Abstract
In a rapidly changing landscape of China’s eco-politics, this is a unique study that aims to explain the China model by highlighting the positive and active roles of bureaucrats between 1978 to the present. It is a pioneering book, representing Wang’s empathetic understanding of China model. It provides causal explanations to question received categories and exposes the taken-for-granted assumptions. Freeing herself from conceptual constraints, Wang avoided all prevailing descriptors of the Chinese economy, such as state capitalism, crony capitalism, or power-capital economy. Even the word “authoritarian” was rarely used. Nor did she rely on the official Chinese term “socialist market economy.” Instead, she reached her tentative conclusion, perhaps quite reluctantly, that China is a “bureaucratic capitalism” (p. 224). I described her choice of this concept as “tentative” because she eluded the accompanying phenomenon of the term—massive corruption—and used the phrase only once. It seems to me that what is described in her book fit better during the economic reform years of 1978–2012 when all Chinese Communist Party apparatchiks, from the top to the bottom, accepted the need for economic reform. Wang exposed the human agency that designed and redesigned economic policy with a focus on the roles of technocrats. However, the slogan “joining the world” during those years meant learning from other nations’ economic practices but resisting substantive political change in Chinese behavior (Cao et al., 2024). This selective borrowing is reminiscent of the period of the Westernization Movement (c1861–1895) during the late Qing Dynasty: borrowing foreign technology for application while relying on Qing Dynasty’s political system—deja vu contention for the status quo. Let us not allow euphemisms to obfuscate that.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".