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Record W3124371131

Converging Trends in Investment Treaty Practice

2012· article· en· W3124371131 on OpenAlexaboutno aff
Karen Cross

Bibliographic record

VenueNorth Carolina Journal of International Law · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsTreatyArbitrationSovereigntyCapital (architecture)State (computer science)Foreign direct investmentIgnoranceInvestment (military)Bilateral investment treatyConvergence (economics)Balance (ability)Law and economicsEconomicsInternational economicsBusinessMarket economyLawPolitical scienceMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Traditionally, the vested interests of states concluding bilateral investment treaties (BITs) fell into two categories: on the side of capital-exporting states, an interest in adopting strong protections for foreign investors; on the side of capital-importing states, an interest in attracting foreign investment but also in attempting to preserve host country sovereignty and authority to promote the public interest. Over the past decade or so, however, the line between capital-exporting and capital-importing state increasingly has blurred, and the calculus for states negotiating BITs has become less certain. The experience of the US as a respondent to claims brought to arbitration by Canadian investors under Chapter 11 of NAFTA significantly affected the development of a new generation of US BITs that better balance the interests of host states against those of foreign investors. Similarly, as emerging market economies become significant exporters of capital, these countries are concluding BITs and resorting to investor-state arbitration, driven at least in part by the needs of their own foreign investors. This article examines this convergence in BIT practice. It suggests that these converging trends, a function of states operating behind what Rawls referred to as a “veil of ignorance,” are having and should continue to have a moderating influence on the content of BITs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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