Applied Agrarian Import Ban and its Impact on Mutual Trade among Russian Federation and European Union & other Selected Countries
Bibliographic record
Abstract
Paper's goal is to provide an overview of Russian import ban impact on trade between Russian Federation and USA, Canada, Australia, Norway and especially EU countries.The paper identifies the changes affecting especially EU agrarian exports performance in relation to Russian Federation.There're changes identified in trade in vegetables, fruits, meat and animal products, dairy and dairy products and fish.There're following findings in the paper: The result of the applied import ban was a significant reduction of Russian agrarian import value (within the first three years alone, the value of imports was reduced by 7,389 million USD).Applied ban affected especially those imports which could be understand as competitors for national production capacities.The potential to substitute those items by local production is evident.The applied ban affected imports especially from Lithuania, Germany, the Netherlands, Denmark, Spain, Belgium, Finland and France.Speaking about the most affected countries, in relation to the share of Russian imports in their trade performance, the most affected countries are Lithuania, Latvia, Estonia, Finland and Poland.To satisfy domestic demand Russia increased food imports especially from Serbia, China, Azerbaijan, Ecuador, Kyrgyzstan, India, Macedonia, Georgia, Bosnia and Malaysia.The negative feature of applied ban especially for Russian consumers was the reduction of food heterogeneity, the growth of food price, the reduction of competitiveness and available food quality reduction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".