Geographical indications and generic names in international trade law: A conditio sine qua non?
Bibliographic record
Abstract
Geographical indications (GIs) have been at the center of international debates for a long time. Recently, several events have led to remarkable developments in the field. The EU has doubled down its efforts in promoting GI protection at the international level. Recent international agreements, such as the 2015 Geneva Act of the Lisbon Agreement on Appellations of Origin and Geographical Indications under the auspices of WIPO, or the bilateral trade agreements with Canada, China, and Japan, among others, have led the EU to claim important victories and progress on the international protection of GIs. Concurrently, the US has stepped up its efforts to put aside market sectors for generic names, i.e., names that are not protectable under GIs. The USMCA between the US, Canada, and Mexico, and the Economic and Trade Agreement between the US and China are good examples of the new US strategy. This article maps these recent developments and argues that GIs have recently taken up a whole new prominence in international trade law. This situation has been accompanied by a change in the stances of the EU and the US. Indeed, the EU appears to have adopted an increasingly rigid stance on GI protection, which has become a conditio sine qua non for multilateral and bilateral negotiations. In turn, and in response to this change, generic names have figured more prominently in the US international trade agenda. This notwithstanding, the gap between the positions of the EU and the US is not unbridgeable since, in reality, both parties legally recognize both GIs and generic names.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".