Bibliographic record
Abstract
Most analysts and commentators portray China's conduct in the South China Sea as a series of aggressive norm violations by an emerging peer competitor to the United States. We argue that this narrative misreads both the substance and dynamics of recent Chinese policy. Since 2016, China has strenuously sought – and largely managed – not to be in technical violation of the Philippines Arbitration Tribunal ruling despite having publicly disavowed it and has attempted to position itself as a champion of win–win co-operation. This stands in stark contrast to the previous four years in which China rather shockingly began asserting itself with little regard for either legality or diplomatic nicety – the period in which the “aggressive China” narrative gelled. What explains China's whiplash behaviour? Why has the international community largely failed to notice recent changes and adjust the narrative accordingly? We argue that the answers to these questions lie in an eclectic appeal to bureaucratic struggles, the regime's two-level game balancing domestic and international pressures, and psychological considerations. These do not, however, provide satisfactory accounts either of China's behaviour or of the international response in the absence of recognising the crucial importance of second-order rules for making, interpreting, and applying first-order rules in the international system. Social practices of rule-making, in short, provide vital context. Our analysis suggests a series of takeaways both for International Relations theory and for managing relations with China.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".