Bibliographic record
Abstract
The purpose of this article was to present sanctions applied to Russia by European Union countries, the United States, Canada, Switzerland and other countries after 2014 as a tool to discourage aggressive behaviour against Ukraine. In addition, an attempt was made to determine the impact of sanctions on Russia’s economy on the basis of Russia’s economic situation analysis. In the opinion of the author of the paper, economic sanctions against Russia have affected Russian economy. The Russian GDP declined, albeit at current prices in the US dollar, the pace of GDP growth was diminished, as well as a global demand, prices and interest rates have risen. There has also been an increase in inflation, the depreciation of ruble and decline in the size of foreign exchange reserves, as well as deterioration of the quality of Russian citizens life. Final conclusion of the paper is author’s conviction that introduction of economic sanctions against Russia and the isolation of Russia on the international stage has led to weakening of Russia’s economic development in the short term. However, over the longer term, the impact of sanctions on the Russian economy has been compensated by mobilization of internal economic growth factors. It is therefore possible to formulate a general conclusion that sanctions applied to small economies will be much severe than to large economies such as Russia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".