Towards a Future Investment Treaty: Lessons from Indirect Expropriation Cases due to Measures to Protect the Environmental and Public Health
Bibliographic record
Abstract
This article considers the formulation of indirect expropriation in the Comprehensive Economic and Trade Agreement between the European Union and Canada (“CETA”) and the European Union – Singapore Free Trade Agreement (“EU-Singapore Trade Agreement”). It identifies the factors to be taken into account by tribunals when adjudicating indirect expropriation claims under these two agreements. Further, it examines how these factors have been interpreted by tribunals in cases which deal with environmental and public health measures. The article argues that the innovations introduced by CETA and the EU – Singapore Trade Agreement have successfully addressed the concerns generated by indirect expropriation cases but some questions still remain unresolved.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".