Bibliographic record
Abstract
This article studies the law of foreign state immunity from a comparative perspective and uses the facts of Huawei-US controversy as a test case to illustrate the differences in the laws of various jurisdictions. The US government has made a number of allegations against Huawei regarding its 5G products and services. The US has also called for the imposition of a ban on Huawei from competing for 5G contracts in a number of common law countries. As a result, Huawei’s business and its reputation are inevitably damaged. Certain allegations against Huawei’s business ethics may be difficult to support due to lack of evidence to support it. This article evaluates the threshold question of foreign state immunity to see if Huawei can sue the US government for defamation. It is concluded that state immunity would block such a claim in most common law jurisdictions, though it may be possible to sue in Canada. It would mean (1) Huawei does not have sufficient legal protection, and (2) it would imply a country may be able to use defamation as an innovative and strategic tool to interfere with commercial competition whilst it may not have any legal consequence. The position on foreign state immunity in China will be discussed for academic comparison.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".