Bibliographic record
Abstract
Chapter 8 analysed the legal responses of some leading states in resisting the extra-territorial application of US economic sanctions by adopting blocking laws and clawback statutes that are designed to neutralise the extra-territorial effect of such laws in the home jurisdiction. Although these laws at least in theory appear to block the extra-territorial assertion of US sanctions in regard to third country trade and investment with US-targeted states, they have had little practical effect in insulating third country businesses from potential liability under US sanctions. Indeed, there are various reasons why third country blocking laws have failed to shield third country entities, but the most prominent reason appears to be that most blocking laws and regulations have not been implemented or consistently enforced in their jurisdictions. Moreover, the Brodie case shows how US authorities have enhanced the effectiveness of extra-territorial sanctions by selectively enforcing sanctions against foreign persons and entities that have used US territory for part of their transactions with US-targeted states, while not attempting to enforce sanctions against foreign persons whose transactions occur solely in another country with blocking laws. Consequently, some major multinational firms with operations in Canada and the European Union have entered agreements with the US government that seek to reduce their liability exposure under US sanctions in return for their compliance with certain requirements of US sanctions law. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".