Rethinking Globalization and the Transnational Capitalist Class: China, the United States, and Twenty-First Century Imperialist Rivalry
Bibliographic record
Abstract
The arrest of Meng Wanzhou and the Huawei prosecution have revealed a mounting battle for high-tech supremacy between the United States and China. The ongoing technology war and the trade war are merely one dimension of a far-reaching and accelerating imperialist rivalry. The changing reality on the world stage has urged a reconsideration of the transnational capitalist class (TCC) thesis and the theory of globalization in general. By reviewing the historical debate between the globalist and critical realist schools, I argue that William Carroll's theoretical frame of global capitalism grounded in corporate network research through emphasizing a dialectical process of the “making” of the TCC is better equipped to explain the unfolding Sino-U. S. conflict. Corporate network research has unveiled a highly regionalized and uneven TCC network: the transnational interlocks of both Chinese and Western corporate directorates are relatively sparse while regional and national ties dominate. It affirms the fragility of the TCC, its internal friction and potential decomposition. It also provides the material ground for analyzing the Sino-U. S. imperialist rivalry as a structural development out of global capitalism and its class 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".