The Significance of Domestic Environmental Regulatory Regimes in Evaluating Breaches of Minimum Standards of Treatment; Lessons Learned from Glamis Gold v. United States
Bibliographic record
Abstract
Domestic regulatory regimes are increasingly important in international commercial and investor-state arbitration. Over a year ago, the arbitral tribunal in Glamis Gold v. United States, found that a Canadian mining company doing business in the United States was treated in accordance with minimum standards of treatment in part due to the strong, relatively transparent environmental regulatory regimes in place. This article argues that Latin American countries would benefit from similarly strong regulatory regimes, especially in cases involving the environment, because they offer transparency and cohesion – two elements that counter accusations of arbitrary and unfair treatment. Part II provides a brief background on Chapter Eleven of NAFTA, relevant U.S. regulatory regimes, and the Glamis decision. Part III explores the minimum standards of treatment framework under NAFTA, identifies elements of regulatory regimes that comply with this framework, and suggests where these are lacking in Latin American regimes. Part IV concludes that Latin American governments should look to the United States as a model for regulation more likely to withstand the scrutiny of alleged violations of minimum standards of treatment in international arbitral tribunals.
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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 | 0.043 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".