Reconceptualizing ISDS: When Is IP an Investment and How Much Can States Regulate It
Bibliographic record
Abstract
Recent victories by states in two investor-state dispute settlements (ISDS), one involving Uruguay’s regulation of tobacco trademarks and the other challenging a doctrine of Canadian patent law, have suggested to some that ISDS is not a threat to state regulation involving intellectual property rights. In this paper, we dispute that notion. We show how these awards open pathways for future disputes and we argue that neither the resolution of these cases nor changes in more recent investment agreements meaningfully alter the threat of ISDS and the chill it imposes on legitimate regulatory activity. We suggest that there would be fewer disputes and less interference with regulatory authority if tribunals took into account the intangibility of intellectual property rights when determining their locus and deciding whether an investment has been made in the host state. Additionally, tribunals should consider the contingent nature of intellectual property rights when calculating the appropriate remedy (if, in short, compensatory relief were substituted for expectation damages). We also argue that if the class of investors entitled to bring ISDS challenges included those who invest in reliance on the limits of protection, right holders would be exposed to the consequences of the principles they advocate and may become less inclined to challenge state action.
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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 | 0.030 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.014 | 0.014 |
| 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".