Glamis Gold, Ltd. v. The United States and the Fair and Equitable Treatment Standard
Bibliographic record
Abstract
This article critiques the arbitral tribunal’s decision in Glamis Gold, Ltd. v. The United States of America on the basis of its interpretation of the fair and equitable treatment standard (FET) owed by state parties to foreign investors under NAFTA article 1105. Part I outlines the post-WWII development of the FET standard in relation to the restrictive, customary international law of minimum standard of treatment (MST). The author traces the expansive treatment of the FET standard by tribunals in both bilateral investment treaty and NAFTA disputes. Despite a binding Free Trade Commission Note of Interpretation limiting the scope of article 1105, NAFTA tribunals had consistently interpreted the FET standard more broadly until the award in Glamis. Part II evaluates the tribunal’s reasoning in Glamis, arguing that it departs from a growing body of jurisprudence on the FET standard under NAFTA without sufficient justification. The author also criticizes the tribunal’s decision to place an unprecedented evidentiary burden on the claimant by requiring proof of both state practice and opinio juris of the FET standard. The conclusion suggests that the decision of the tribunal in Merrill & Ring Forestry L.P. v. Canada may provide a better approach to balancing governments’ legitimate regulatory objectives and foreign investors’ treaty rights.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".