Inter-State Competition and Transnational Capitalists across the North-South Divide: Different Strategies, New Configurations of Power
Bibliographic record
Abstract
Abstract Existing research emphasizes the rise of new coalitions of states from the global South, such as the BRICS, on one hand, and the rise of a transnational capitalist class on the other. Yet only a small body of work has considered how transnational capitalists shape, and are shaped by, inter-state competition. Research neglects explicitly comparative inquiries: do transnational capitalists in the global North and the global South interact with inter-state competition differently? This article analyzes two cases in which transnational firms in the global North and South attempted to shape state institutions at domestic and international levels to strengthen trade liberalization. We argue that, while firms in the global North and South increasingly share preferences for trade liberalization, the way in which these firms pursue that goal is shaped by their historical relationship to the institutions of U.S.-led hegemony. Transnational firms in the global South seek to foment inter-state competition in order to decenter U.S. leadership, while their counterparts in the North seek to minimize and accommodate inter-state competition to preserve U.S. leadership and their own private authority. Both the North and the South are contributing to the geographical re-centering of institutional power in the world economy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".