STRATEGIC STALEMATES: Explaining Gray Zone Assertions of China in the South China Sea
Bibliographic record
Abstract
The South China Sea is one of the world’s most resource rich and geo-strategically important regions. Therefore, it is not a surprise that China has sought expansionist claims in this region to increase its power through its controversial nine-dash line. These assertions directly clash with the sovereign claims of several other littoral states in the region, namely Indonesia, Malaysia, Vietnam, the Philippines, Taiwan and Brunei. China remains unyielding in maritime disputes in the East and South China Seas, underscoring its strict stance on these strategically valuable regions. This thesis attempts to understand why these claimant states in the South China Sea are showing no signs of explicit balancing or alliance building against rising Chinese aggression in the region. I propose that this behavior might be explained by a “two layered gray zone approach” adopted by China. This approach includes economic coercion (through a “strings attached” investment model) and covert/hybrid aggression (through armed civilians, maritime militias and coast guards). These strategies make China less accountable to the international community and essentially binds these States to be economically linked with Beijing with no viable strategies to escalate militarily due to the civilian component of aggression creating an asymmetry between contesting states and China. The thesis will test this theory on four key claimant states in the region- Vietnam, the Philippines, Indonesia and Malaysia. The thesis will explore whether cases with a higher level of 'elite capture'—i.e., a more dependent elite network with Beijing—display a lower level of aggression in the South China Sea
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".