New challenges and opportunities for Italian exports of table wines and high quality wines
Bibliographic record
Abstract
Competition in international wine market has recently become more intense because of several factors and, in particular, the progressive reduction in world-wide consumption, the addition of new producing countries such as Australia, Chile, the USA and South Africa (the so called New World wine producers) and the increasing trade liberalization.In order to achieve a competitive advantage in the international marketplace, it is very important to identify which markets are characterized by bigger attractiveness.In particular, the ability of a producer, a region or a country to provide effective communication and promotion actions towards more profitable local markets is strategic in international trade.Italy, one of the first wine producing country in the world, exports its wines to almost all countries of the five continents.However, the five greater importing countries account for 70 percent of Italy's total wine exports (the USA, Germany, the United Kingdom, Switzerland, and Canada) and so Italy shows only a moderate market diversification.In this paper, an econometric model able to explain the size of wine export flows from Italy to its main importing countries was elaborated and estimated.This model provides useful information that can help to identify the main growing markets where all participants in the wine supply-chain, such as private wineries, joint-ventures, regional and national agencies, and producers' associations, can unite to concentrate product communication and promotional efforts.The model is an extended version of the "gravity model" that many economists believe a very powerful tool for international trade analysis.In fact, at the empirical level, the gravity model gives very robust estimates and provides a good fit to the observed data.The basic concept of the gravity model for trade analysis borrows the gravity equation from physics: the
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".