What the Ecuador–US award tells us about the potential for more state–state investment arbitration
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".