MétaCan
Menu
Back to cohort
Record W4395468905 · doi:10.54648/joia2024008

Article: The FET Standard between Treaty Reform and ISDS Practice: An Analysis of the Modernized ECT

2024· article· en· W4395468905 on OpenAlexaff
Alessandro Monti, Matteo Fermeglia

Bibliographic record

VenueJournal of International Arbitration · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsTreatyPolitical scienceLaw and economicsLawSociology

Abstract

fetched live from OpenAlex

The Modernization of the Energy Charter Treaty (ECT) counts as a prominent attempt to better incorporate climate change considerations into an investment treaty that is highly impactful from a climate change perspective. Among other initiatives, reform efforts in the ECT Modernization have led to amendments of key standards of treatment providing the substantive legal basis for claims by foreign investors. Focusing on the Fair and Equitable Treatment (FET) standard, this article contrasts the new treaty provision under Article 10 of the ‘modernized’ ECT with consolidated interpretations of the FET standard in previous arbitral practice. Building on such a jurisprudential analysis, this article evaluates the extent to which reformed standards of investment protection in the Modernized ECT can lead to an increased likelihood of climate-aligned outcomes in investor-State disputes, thereby providing an analytical assessment as to the potential of the newly introduced ECT provisions on FET – which may also serve as benchmark for further reforms of international investment agreements (IIAs) – to expand regulatory space for host States and support the adoption of more stringent climate policies. Fair and Equitable Treatment, Energy Charter Treaty, Modernization, Investor-State Dispute Settlement, Climate Change, Regulatory Space, Investment Arbitration, Fossil Fuels, Legitimate Expectations, Treaty Reform

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.418

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 communication0.0000.002
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 head0.016
GPT teacher head0.302
Teacher spread0.286 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of International ArbitrationSame topicTaxation and Legal IssuesFrench-language works237,207