Mapping the New Frontier of International IP Law: Introducing a TRIPs-plus Dataset
Bibliographic record
Abstract
Abstract This article introduces a new dataset on the intellectual property (IP) provisions included in preferential trade agreements (PTAs) and makes it available for research and policy communities alike. Several PTAs include IP commitments that go well beyond the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs). A sound knowledge of these TRIPs-plus commitments is essential in order to improve our understanding of what drives them and of their legal, social, and economic consequences. Yet, until now, these provisions have not been mapped in a comprehensive and systematic way. The T + PTA dataset fills this gap by documenting the existence of 90 types of IP provisions in 126 agreements signed between 1991 and 2016. We show that, even for like-minded countries, significant variations exist in their reliance on TRIPs-plus provisions, their degree of consistency across PTAs, and their preferences for some IP rights. We also find that strong TRIPs-Plus provisions are correlated with the depth of PTAs, the asymmetry between trade partners, and the strength of their domestic IP law. By making the T + PTA dataset available, we hope to create the opportunity for a new generation of research on TRIPs-plus agreements.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".