Index-based Analysis of Georgian Wine Export's Competitiveness on a Global Market
Why this work is in the frame
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Bibliographic record
Abstract
The main objective of the article is to determine competitiveness of Georgian wine exports. Initially, it is shown the current situation of Georgian wine export industry. In analysis part it is taken first 28 countries, where Georgia exports the wine and the period is defined from 2008-2018. In the methodological part, in order to determine competitiveness of Georgian wine it is used the Trade Intensity Index, Revealed Comparative Advantage Index (Balassa Index) and the competitiveness Index of Wine Exports between Georgia and other countries. Based on the results and discussion it should be concluded that Georgia has revealed comparative advantage in the international wine market. In the international market it is difficult for Georgia to compete large-volume wine production EU countries: France, Italy and Germany. The revealed comparative advantage for USA and Canada is low on the international wine market. However, it is hard for Georgia to compete with the USA wines on the international market, while Georgian wines are more competitive than Canadian.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it