China’s Asymmetric Statecraft: Alignments, Competitors, and Regional Diplomacy <i>by Yuxing Huang</i>
Bibliographic record
Abstract
Interpreting shifts in a nation’s foreign policy can be a difficult task in the absence of direct access to leaders’ strategic thinking. At any rate, the complexity of geopolitical posturing and jockeying for position when multiple actors are involved do not lend themselves to straightforward answers. Yet, political scientists continue striving to find parsimonious explanations: ones that explain a state’s behavior with reference to one or a small set of variables. In China’s Asymmetric Statecraft, Yuxing Huang seeks to explain China’s statecraft toward its mostly smaller and weaker neighbors by positing that the number of regional rivals is the decisive factor. When there is one rival in a region, he claims, China adopts a uniform approach to its asymmetric statecraft with nonallied neighbors. This is intended to present an attractive image of fairness and consistency. At times when there are two or more rivals, China switches to a selective approach, tailoring its policy to each state. In each case, the uniform or selective approach is supposed to encourage the state to lean toward China rather than the competition. On the other hand, Huang claims, the reverse is true with regard to regional allies: a uniform approach when there is more than one competitor and a selective approach when there is only one.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".