European Wine Exports: The Key Role of Trade Policy
Bibliographic record
Abstract
Summary Wine exports from the European Union have gained shares in both traditional markets ( US , Canada, Switzerland and Japan) and in new markets (China, Hong Kong and Russia). In coming years, major growth in consumption and imports is expected in these new markets and in others, like Singapore, South Korea, India, Brazil, Mexico, South Africa and Angola. Most of the above new markets, however, are still heavily protected by high tariff and non‐tariff barriers, the most intrusive being wine labelling regulations, oenological practices, maximum residue limits of agrichemicals, certification and testing procedures. Since the stalling of the Doha multilateral negotiations exporting countries have been negotiating Free Trade Agreements to gain preferential access to fast growing markets. The EU has signed several agreements relevant to the wine trade (e.g. with South Korea, Singapore and Canada) and a number of negotiations are currently in progress. However, some of the EU 's main competitors, in particular Chile, Australia and New Zealand, are very active as well. EU wine exporters would therefore be aided by an effective trade policy strategy, otherwise they risk losing ground to their competitors. The timely conclusion of the agreements currently under negotiation (as with India) and a prompt start to negotiations with China would therefore be desirable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".