Intellectual Property and International Dispute Resolution
Bibliographic record
Abstract
When a couple of years ago, intellectual property laws had their first brush with investor-state dispute settlement, the encounter was not considered a particularly welcome one: intellectual property laws operated on the principles of national treatment, transparency (patere, Latin for: stay or remain open) and judicial expertise, while ISDS was a specific mechanism open only to foreign investors operating in secret by panels of trade lawyers chosen ad hoc. Unsurprisingly, experts in the field of international trade, and those in the field of intellectual property law that contributed to this book held widely differing views on the merits of ISDS, and whether intellectual property rights should be included as “investments” in bilateral and multilateral investment treaties that envisaged such form of dispute settlement. Undoubtedly, however, the high profile cases concerning plain packaging laws for tobacco products, and the Eli Lilly v. Canada case in the context of NAFTA, have stirred a wider debate on the appropriateness of ISDS particularly amongst developed nations. Trade negotiations of the EU with Canada for the CETA Agreement and with the US for TTIP were accompanied by significant public concerns that foreign investors via ISDS would be allowed to run roughshod over European standards for protecting health, consumers and the environment. The contributions of this book highlight the different mechanisms of international dispute settlement having particular regard to cases involving intellectual property laws.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".