Expropriation Under NAFTA Chapter 11 Investment Dispute Settlement Mechanism: Some Comments on the Latest Case Law
Bibliographic record
Abstract
The question regarding the constituent elements of expropriation of an investment under international law is the object of much debate and controversy. Although being an essential concept in international law, there remains great ambiguity surrounding the notion of The purpose of this article is not an analytical description of NAFTA Article 1110. Instead, the aim is to give practitioners an overview of recent NAFTA Chapter 11 cases which have interpreted the notion of By the time this paper was published in 2002, one final award (Metalclad v Mexico) and two partial awards (Pope & Talbot v. Canada; SD Myers v. Canada) had been rendered. This paper describes the factual considerations involved in these three cases as well as an analysis of the reasoning of the tribunals, particularly with respect to the interpretation given to Article 1110 and the use of the words measures tantamount to nationalization or expropriation. These three arbitral tribunals have adopted fairly different views on what constitutes an expropriation under NAFTA. This paper argues that although the text of NAFTA Article 1110 does not in itself depart from existing international law on the subject, some statements made by these arbitral tribunals have clearly given an interpretation to the concept of expropriation that is more extensive than interpretations currently prevailing under international law.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.022 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".