Lawfare as a Policy Tool in Sino-American Relations: The Case of Huawei CFO Meng Wanzhou
Bibliographic record
Abstract
Competition between the United States and China is at an all-time high. Despite decades of diplomacy between the East and West, recent trends suggest the two powers are drifting further apart. To understand US-China relations, it is critical to understand major developments as they occur. This paper examines the geopolitical significance of United States v. Meng (2020), an extradition case in which US authorities requested the transfer of Chinese tech executive Meng Wanzhou to American jurisdiction. Despite US policymakers declaring Meng and Huawei to be threats to national security, the eventual dismissal of all charges Meng faced presents a puzzle to policymakers and academics alike, who wonder why such a high-profile case could be dropped years later. Through analyzing primary sources from the United States, China, and Canada, I conclude that the trial in question stems from larger geopolitical themes of US-China competition and represents an example of lawfare being used as US foreign policy tool. This trial has wide-reaching implications for the foreign policy strategies of the United States and China, notably in the economic, technology, and security arenas, and represents a rift from the international laws and norms surrounding sanctions, which have typically been a multilateral affair.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".