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Record W2496420760 · doi:10.1017/cbo9781316481479

The Formation and Identification of Rules of Customary International Law in International Investment Law

2016· book· en· W2496420760 on OpenAlexaff
Patrick Dumberry

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCustomary international lawInternational investmentArbitrationInvestment (military)International lawLawIdentification (biology)State responsibilityForeign direct investmentState (computer science)Political scienceLaw and economicsBusinessEconomicsPublic international lawComputer science

Abstract

fetched live from OpenAlex

Rules of customary international law provide basic legal protections to foreign investors doing business abroad. These rules remain of fundamental importance today despite the growing number of investment treaties containing substantive investment protection. In this book, Patrick Dumberry provides a comprehensive analysis of the phenomenon of custom in the field of international investment law. He analyses two fundamental questions: how customary rules are created in this field and how they can be identified. The book examines the types of manifestation of State practice which should be considered as relevant evidence for the formation of customary rules, and to what extent they are different from those existing under general international law. The book also analyses the concept of States' opinio juris in investment arbitration. Offering guidance to actors called upon to apply customary rules in concrete cases, this book will be of significant importance to those involved in investment arbitration.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.017
Scholarly communication0.0110.009
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.194
Teacher spread0.181 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations20
Published2016
Admission routes1
Has abstractyes

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