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Record W4284881182 · doi:10.1163/22119000-12340252

Why Due Regard Is More Appropriate than Proportionality Testing in International Investment Law

2022· article· en· W4284881182 on OpenAlexaboutno aff
Caroline E. Foster

Bibliographic record

VenueThe Journal of World Investment & Trade · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsProportionality (law)RationalityLawPolitical scienceArbitrationInternational investmentLaw and economicsInternational lawEconomicsDue diligenceForeign direct investment

Abstract

fetched live from OpenAlex

Abstract Global regulatory standards of due diligence, regulatory coherence, and due regard are emerging in public international law. Investment law has been concerned to settle upon the most appropriate regulatory coherence tests for application in the arbitration of regulatory disputes. Candidates have included proportionality, rationality, and reasonableness tests. This article argues instead for reliance on the due regard standard in conjunction with reasonableness or rationality testing. This will more appropriately reflect the nature of investment treaties as inter-State bargains. Further, responding to arguments for the adoption of proportionality on the basis of comparative public law, the article demonstrates that proportionality is not established as a general head of review in common law jurisdictions including England, Australia, Canada, New Zealand and South Africa. At the same time, the application of the due regard standard can have much in common with procedural proportionality testing as seen among these domestic legal systems and elsewhere.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.248
Teacher spread0.213 · 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 teacher head, not a consensus.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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