The Protracted WTO Battle over a Multilateral GI Register: What Lies Beneath?
Bibliographic record
Abstract
The November 2014 breakthrough on the Trade Facilitation Agreement might have gone some way in infusing a new lease of life to the WTO as an institution, the same can hardly be said for most of the other Doha Round issues that still appear to be trapped in a blind alley. The issue of the creation of a multilateral register of Geographical Indications (GIs) for wines and spirits is one such area. Despite several years of wrangling, arriving at a landing zone on this issue still appears to be a far-fetched dream, if the mood expressed by the WTO Members in the December 2014 informal meeting on the multilateral register is anything to go by! Written against this backdrop, the present article has two objectives. First, it makes an attempt to trace the historical, legal as well as economic reasons underlying the protracted debates on GIs under the WTO and beyond – which have widely been referred to as one between the ‘Old World’ (e.g., the EU, Switzerland) and the ‘New World’ (e.g., the US, Australia, Canada, Argentina, Chile, etc.). Second, the article provides a snapshot of the long-drawn WTO negotiations on the multilateral register for wines and spirits under the Doha Round.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".