Realism, liberalism and regional order in East Asia: toward a hybrid approach
Bibliographic record
Abstract
East Asia offers a fertile ground for applying dominant theoretical perspectives in International Relations and understanding their relevance and limitations. As this region has seen much conflict and cooperation historically and is re-emerging as a key theater of great power competition in the 21st century even when states maintain high levels of economic interactions, our understanding of the regional order will be enhanced by the theoretical tools available in the larger mainstream IR perspectives. The existence of a peculiar regional order of no war, yet a number of simmering disputes (along with high levels of economic interdependence) can be characterized as cold peace which deserves an explanation. The paper applies two variants of realism—balance of power and hegemonic stability – and the key arguments in liberalism to analyze the cold peace in Northeast Asia and normal peace in Southeast Asia from a historical perspective. It finds both grand theoretical approaches have partial applications for understanding the East Asian order. A hybrid approach is more valuable to better explain regional order during diverse time periods and different sub-regions of East Asia. Although the presence of both hegemony and balance of power can prevent major wars for a period, they do not help resolve the pre-existing disputes. Deepened economic interdependence mitigates some spiraling tendencies as states fearful of losing too much economically do not escalate crises beyond a point.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".