Fair and equitable treatment of foreign investments and intellectual property rights
Bibliographic record
Abstract
In assessing the potential impact that treating intellectual property (IP) as an investment asset can have on the IP policy space available to states, it is essential to draw a distinction between the rules governing the expropriation of investment assets and the rules relating to the fair and equitable treatment (FET) of investments. This distinction is necessary because, in some investment agreements, measures relating to IP are excluded from the scope of the rules on expropriation, whereas there is usually no such exclusion of IP from the scope of the FET standard. Moreover, the FET standard can be described as a standard whose content and scope is ambiguous, thus making it a potentially useful tool in the hands of an investor seeking to challenge an IP measure adopted by a state. This chapter therefore seeks to examine the potential impact that the FET standard can have on the IP policy space available to states. Using the two recent decisions of investment tribunals in the cases of Philip Morris v Uruguay and Eli Lilly v Canada as case studies, the chapter will critically examine the extent to which a claim based on a denial of FET can narrow down the policy space available to states to design their national IP laws in a way that suits their level of development and societal needs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".