Drawing the Short Straw: Disproportional Effects of Russian Sanctions on Central Europe and the Baltic States
Bibliographic record
Abstract
In 2014, the agricultural sanctions Russia imposed on the European Union (EU) had a perceivable impact on the EU’s economy. Yet the sanctions arguably had a disproportionate impact, which suggests they were particularly successful in exposing underlying issues within the EU. Specifically, former Soviet bloc countries and southern European countries were far more greatly impacted by the sanctions than the larger western EU member states. This brings to light problems of disproportionate representation of member states within decision-making processes (especially within the Committee for Agriculture and Rural Development), and the fragility of the EU's internal cohesion. By comparing typical decision-making processes of the EU with its responses during times of crisis, it becomes clear that the EU’s decision-making process and its internal cohesion with regard to economic assistance for former Soviet states, are vulnerable to Russia’s actions. The essay will conclude with recommendations on how to improve EU decision-making during times of crisis to counter this vulnerability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".