Bibliographic record
Abstract
This article explores European Union (EU) policy on geographical indications (GIs) as expressed in the outcomes of EU trade negotiations. This empirical approach provides a factual basis about the GI deals which are acceptable to the EU. Across the EU’s six recent Global Europe treaties the EU has achieved a good degree of success in obtaining strong-form GI rights (no use of -like, -style qualifiers on labels) for a number of specific products. The article also identifies GI outcomes in recent treaties driven by US negotiating demands. While US-driven treaties prioritize a trademark approach to GIs, they also allow for coexistence with EU-style strong-form GIs. Comparing these two sets of outcomes provides useful insights for future EU trade negotiations, such as the proposed Transatlantic Trade and Investment Partnership (TTIP) with the US or the proposed Free Trade Agreement with Australia and New Zealand. In particular the Canada-EU Comprehensive Economic and Trade Agreement (CETA) shows how the interests of domestic cheese and meat producers can be protected while allowing for strong-form GI privileges for a reasonable number (163 in CETA) of listed product names.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".