The political economy and dynamics of bifurcated world governance and the decoupling of value chains: An alternative perspective
Bibliographic record
Abstract
Employing insights from political economics, international relations, and China studies, we identify the key variables that shape the dynamics of the U.S.-China rivalry and investigate their impacts on the bifurcation and value-chain decoupling processes. We show that the ongoing conflict and disengagement processes are more likely to evolve in the long run in significantly different ways to the one envisioned by current Washington decision-makers and echoed by Petricevic and Teece (2019). The latter predicted an escalation of the disengagement processes and inevitable convergence to a 'bifurcated world'. Our main findings are: (1) The potential costs of bifurcation and consequent value-chain decoupling are prohibitive to both China and the U.S. Resistance is likely to grow by U.S.' own MNEs and allies; (2) Washington decision-makers overstate the threats that 'China's rise' poses to the survival of the liberal world order; and (3) China's techno-nationalistic threats are likely to dissipate after a period of escalation, as a result of its own resource constraints, increasing costs of key programs, and inability to sustain in the long run its rapid innovation processes due to growing central controls. We conclude the paper by outlining an approach to maintain an open global economy and secure innovation systems.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".