MétaCan
Menu
Back to cohort
Record W4381186034 · doi:10.1093/arbint/aiad036

What the Ecuador–US award tells us about the potential for more state–state investment arbitration

2023· article· en· W4381186034 on OpenAlexaboutno aff
Andrea K. Bjorklund

Bibliographic record

VenueArbitration International · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsArbitrationState (computer science)Investment (military)Investment arbitrationLawPolitical scienceInternational investmentComputer sciencePoliticsForeign direct investment

Abstract

fetched live from OpenAlex

In late 2016, the award in Ecuador v United States was finally released, more than four years after it was issued. While the Ecuador v United States case is unlikely to have the resonance of the Alabama Claims arbitration, about which Rusty Park wrote in such gripping detail in this journal,1 studying it is an opportunity to pay homage to Rusty as well as to assess the advantages and disadvantages of state–state investment arbitration as the international community grapples with potential alternatives to investor–state arbitration. Investor–state arbitration—the process whereby an investment treaty between two states permits an investor from one of the treaty parties to submit a claim to arbitration for violations of the investment treaty by the other state party—has been criticized on many grounds.2 These criticisms include, but are not limited to, complaints that arbitral tribunals convened under these treaties are composed of private individuals who make decisions that affect the public interest, that their decisions intrude upon state sovereignty, and that they empower large corporations to act against small developing countries who cannot afford the price of the arbitration, let alone the ensuing award.3 These criticisms have prompted discussions about possible alternatives. One of the alternatives is state–state arbitration—to place the dispute on the international plane in a procedure between the foreign investor’s home state and the host state in which the investment was made.4

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)0.006
metaresearch head score (Gemma)0.041
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0110.012
Open science0.0010.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0620.005

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 head0.260
Teacher spread0.242 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueArbitration InternationalSame topicInternational Arbitration and Investment LawFrench-language works237,207