Island territorial disputes and China’s ‘Shelving Disputes and Pursuing Joint Development’ policy
Bibliographic record
Abstract
‘Shelving Disputes and Pursuing Joint Development’ (SDPJD) has for decades been China’s premier policy for resolving territorial disputes, especially those regarding islands. SDPJD is, however, commonly understood to be tripartite policy, in which ‘shelving disputes’ and ‘pursuing joint development’ are made conditional upon the principle of ‘sovereignty belongs to China’. Although SDPJD aims to peacefully settle China’s island territorial disputes in the East China Sea (Diaoyu Islands) and the South China Sea (Spratly Islands), the policy has not been particularly successful in practice. This is in part because, whereas ‘shelving disputes’ and ‘pursuing joint development’ are cooperative in nature, ‘sovereignty belongs to China’ is inherently confrontational. The prominence granted to ‘sovereignty belongs to China’ is linked to outmoded understanding of the concept of sovereignty and the tendency for Chinese scholars and officials to regard island territorial disputes as the zero-sum games. This paper argues that SDPJD’s success is dependent upon separating ‘sovereignty belongs to China’ from ‘shelving disputes’ and ‘pursuing joint development’ and perhaps abandoning the former principle entirely. China should pursue non-confrontation resolution to island territorial pursuits, especially in the contexts of efforts to develop a peaceful and cooperative 21st-Century Maritime Silk Road.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".