The Huawei DPA: A Prologue to the Global Arrest Game?
Bibliographic record
Abstract
On Sept. 24, 2021, the U.S. Department of Justice (DOJ) announced that Meng Wanzhou, Chief Financial Officer of Chinese telecom giant Huawei, had entered into a deferred prosecution agreement (DPA) on charges of conspiracy to commit bank fraud and conspiracy to commit wire fraud, bank fraud and wire fraud. In so doing, Meng admitted to making material misrepresentations to U.S. financial institutions about Huawei’s business activities in Iran. According to the terms of the DPA, DOJ agreed to withdraw its request for Meng’s extradition from Canada and recommend to the Eastern District of New York (EDNY) that the court release her on a personal recognizance bond, with the understanding that all charges will be dismissed in December 2022 if she does not commit any additional federal, state, or local crimes.\nThe development ends a “damaging” trilateral U.S.-China-Canada standoff at the intersection of U.S. criminal justice, foreign relations, and international law. The good news is that it cools a source of longstanding tension between the three countries. The bad news is what it may portend for the future of U.S. extraterritorial law enforcement policy, both at home and abroad.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".