Agrarian import ban and its impact on the Russian and European Union agrarian trade performance
Bibliographic record
Abstract
The main objective of the paper is to identify the impact of the Russian agrarian import ban on imports of certain agricultural products from Europe, Norway, Canada, the USA and Australia. The import ban particularly affected the import of competitive agricultural and foodstuff commodities into Russia. The ban was the result of the political and security tensions existing between the Russian Federation and the Western World, since early 2014. If we take into consideration the volume of the mutual trade and the importance of the mutual trade for each of the stakeholders mentioned above, it can be assumed that the main objectives of the Russian embargo was not to hurt the individual countries - especially the European Union, but rather to support domestic producers in order to achieve a higher level of self-sufficiency in basic agricultural products. It is evident that the objective of the applied import ban is to reduce the dependence of the Russian Federation on imports. This paper provides an analysis of the most affected product groups and products, especially in relation to the EU countries. The paper provides the following findings: The result of the applied import ban was a significant reduction of the Russian agrarian import value. The import ban also improved the overall competitiveness of the Russian agricultural trade, whilst on the other hand, the competitiveness of some commodity groups was reduced.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".