Corporate Power in Global Agrifood Governance – Edited by Jennifer Clapp and Doris Fuchs
Bibliographic record
Abstract
Scholars of Chinese politics, political economy, and modern history should find this book a useful addition to existing scholarship on China's fourth-generation leadership.This book also provides insights for those studying China's economic modernization.The final chapter notes the difficulty much of the technocratic leadership has in adapting to the market economy.It may be that China's innovation and technological strengths and weaknesses have arisen in part because of the training of their leaders as engineers and not as entrepreneurs.However, it is also in this chapter that Andreas arguably misinterprets the challenges faced by the Red Engineers and their progeny in the new economy.Rather than weakening the hold of China's existing technocratic class, reform may instead have strengthened their control.China's richest citizens are the heirs of political leaders.Political capital has transferred itself into economic capital.It may be that cultural power is declining in importance relative to political and economic, but in many cases there remains a fair degree of overlap.While the current technocratic class is likely to become less technocratic over time and more business and economic capital oriented, the membership will remain largely the same.Nonetheless, as Andreas notes, Tsinghua will remain at the top of this structure, training the next generation of China's leaders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".