Eli Lily and Company v The Government of Canada and the Perils of Investor-State Arbitration
Bibliographic record
Abstract
We live in a world today where it is routine for foreign private companies to sue sovereign countries, claiming that domestic laws interfere with foreign investment activities. Take the case of Eli Lilly v the Government of Canada. Eli Lilly and Company (“Eli Lilly”), a multinational pharmaceutical corporation, is presently suing the Government of Canada (“Canada”), alleging that the invalidation of two patents amounts to an unlawful expropriation of Eli Lilly’s intellectual property. The company claims Canada’s patent laws are arbitrary, discriminatory, and in breach of the minimum standard of treatment owed to foreign investors under the North American Free Trade Agreement (“NAFTA”). The company is seeking damages in excess of half a billion dollars.1
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.010 |
| 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".