Understanding change in times of crises: US–China competition and the prospects for peaceful change
Bibliographic record
Abstract
Abstract This essay explores the nature, causes, and prospects of three types of international order transitions: systemic, institutional, and systems transitions. It argues that peaceful change remains possible despite growing turbulence in the context of US–China competition. Historically, systemic transitions, marked by shifts in the distribution of power among great powers, were often driven by crises and wars. Today, however, nuclear deterrence, defensive military technologies, and the increasing agency of non-great powers create greater space for nonviolent systemic transitions. At the institutional level, states are pursuing strategies such as soft balancing, economic statecraft, and informal multilateralism to manage great power competition and shape rules, norms, and practices, fostering the possibility of peaceful institutional transitions in global governance. Looking further ahead, the rise of artificial intelligence and other emerging technologies may diffuse power beyond states, empowering corporations, NGOs, and other non-state actors. This could drive a more profound systems transition, fundamentally altering the structure and actors of the international order itself. While risks of technological overreach and geopolitical conflicts persist, this essay concludes that the trend toward low-violence great power rivalry, the growing agency of non-great powers, and the diffusion of critical technologies can collectively steer the international system toward more peaceful transitions in the context of US–China competition than in previous eras.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".