International investment law and data, copyrights and performance requirements: a closer look at <i>Einarsson v Canada</i>
Bibliographic record
Abstract
... The interface between international investment law and IP law is continuing to develop.1 After high profile investor–state dispute settlement (ISDS) cases, in which investment tribunals decided disputes involving trade marks and patents,2 copyrights and data are next. In Einarsson v Canada, the Claimants argue that Canada—through legislation and government action—confiscatedtheir IP rights (IPRs) in seismic data in breach of investment protection standards provided for in NAFTA.3 This is the first known case in which copyright and data4 issues arise in investment arbitration, so the tribunal’s decision will have broad implications for the development of this area of law. With more than US$2.5 billion claimed, the financial stakes are extremely high as well. Against this backdrop and on the basis of existing case law, this article aims to analyse the many questions relating to IP protection under investment law that are still unresolved. The legal issues range from the question, whether and under what circumstances copyrights and data constitute a covered investment under international investment law, to the extent and contours of the level of protection offered to those investments under international law (specifically under the fair and equitable treatment (FET) standard and the prohibition against uncompensated expropriations) and its relationship with domestic law and international and multilateral IP treaties. In a first, the prohibition against performance requirements is also at issue. The case also showcases the continuing propertization and expansion of IP protection through investment arbitration from patents and trademarks to copyrights and data. The article thus also offers a case study for the general critique offered by some commentators in this regard.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".