Investor-State Dispute Settlement: Human Rights and Regulatory Lessons from <em>Lilly v Canada</em>
Bibliographic record
Abstract
The triangular interface between trade, IP and human rights has yet to be fully formed, both doctrinally and normatively. Adding investor-state dispute-settlement (ISDS) to the mix increases the complexity of the equations to solve. Two resultant issues are explored in this Article. First, the Article considers ways in which broader public interest considerations-in particular human rights -- can and should be factored into determinations of a state’s action compatibility with its trade obligations and commitments in a state-to-state dispute-settlement context. Second, the Article examines whether doctrinal tools used in state-to-state trade dispute-settlement to make room for public interest considerations port to the investment/ISDS context. The Article uses the recent Lilly v Canada case as backdrop to illustrate the points made. The Lilly case dealt with an ISDS complaint filed after the revocation of two Canadian patents on pharmaceutical products. The Article approaches the above-mentioned triangular interface from a policy perspective that factors in innovation and investment protection but also public health, a policy area supported by a human right (to health) and in which states need regulatory autonomy.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".