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Jurisdictional Countermeasures Versus Extraterritoriality in International Law

2016· article· en· W2593476741 on OpenAlexaboutno aff
Seyed Yaser Ziaee

Bibliographic record

VenueRussian Law Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsExtraterritorialityLawStatuteJurisdictionPolitical scienceInternational lawSanctionsConflict of lawsState (computer science)SovereigntyComityUniversal jurisdictionMunicipal law

Abstract

fetched live from OpenAlex

Sovereignty is the reason why States seek to apply their jurisdictions. All States like to extend their jurisdictions as far as they can, so some of them have adopted extraterritorial policies in exercising their jurisdictions. In this manner the United States has approved several extraterritorial Laws in respect of competition law and sanctions, causing some coercion to non-target states. In response to this long-arm jurisdiction by the U.S., some countries, such as the U.K., Canada, Australia, Mexico etc., as well as the E.U., took actions of their own in order to nullify these extraterritorial laws. These measures, which are mostly applied to the jurisdictional field, could be described as jurisdictional countermeasures. They can be divided into prescriptive, adjudicative and executive measures, which include blocking statutes, claw-back statutes, non-recognition, procedural restrictions, non-execution and retaliatory measures. Not all of these measures are prohibited by international law and some can be viewed as a just retorsion against that State. However, where the application of these measures is prohibited by international law – in cases such as the non-recognition of foreign judgments and other jurisdictional regulations in international treaties like mutual judicial assistance agreements – they are countermeasures. If these actions are in response to an illegal extraterritorial law, they should comply with the conditions for countermeasures as cited in the Draft Articles on Responsibility of States for Internationally Wrongful Acts 2001 as approved by the International Law Commission.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.048
Scholarly communication0.0110.010
Open science0.0020.006
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.023
GPT teacher head0.253
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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