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Record W4400935404 · doi:10.54648/eila2024025

Article: The 2019 BLEU Model BIT: BLEU’s Vision of the Future of Investment Protection

2024· article· en· W4400935404 on OpenAlexaboutno aff
Max Kremer, Paschalis Paschalidis, Dano Brossmann, Peter Vedev

Bibliographic record

VenueEuropean Investment Law and Arbitration Review Online · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsBLEUComputer scienceBit (key)Artificial intelligenceNatural language processingComputer securityMachine translation

Abstract

fetched live from OpenAlex

This article compares the 2019 BLEU model bilateral investment treaty with the older 2002 version. After an overview of the BLEU’s role as a major actor in international investment law, the authors argue that the changes introduced by the 2019 Model BIT reflect the EU’s policy towards foreign investment protection. The drafters of the 2019 Model BIT were inspired by the EU-Canada Comprehensive Economic and trade Agreement and the European Commission’s proposal to establish a Multilateral Investment Court. The 2019 Model BIT marks a shift from liberalization of foreign direct investment (FDI) to the protection of the sovereign powers, in particular the State’s right to regulate, and the curtailing of investment protection presented as a response to the investment protection system’s misuse and abuse by investors. By placing certain limitations to substantive standards of protection, placing strong emphasis of sustainable development objectives and advocating a reform of the investor-State dispute settlement (ISDS), the 2019 BLEU Model BIT puts forward a vision for the future of investment protection that departs from the Washington Consensus of economic liberalization but also enables the investment protection system to survive in the face of the so-called backlash against ISDS and the criticism of civil society.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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