Bibliographic record
Abstract
After more than four decades of economic reform, China is now the world's second-largest economy and a much-desired business partner for multinational corporations around the world. In Mao and markets, two business researchers critique some of the conventional wisdom about contemporary China and offer advice for international businesses seeking to trade with and invest in the country. As a starting-point, the book questions two familiar beliefs: first, that free-market economies are more efficient than state-controlled economies; and second, that the development of a market economy will lead to freedom in other spheres of life, including political freedom. As Christopher Marquis and Kunyuan Qiao rightly point out, China has experienced remarkable economic growth since the 1980s while the state has maintained considerable control over the economy. Furthermore, one-party rule has not collapsed following market-oriented economic reforms in the country; instead, the Communist Party of China has consolidated its power in recent years. The authors argue that, to make sense of China's successful combination of market mechanisms and state control, it is necessary to appreciate the history of the country, especially the enduring influence of founding leader of the People's Republic, Mao Zedong.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".