Sankcje wobec Rosji a gospodarka rosyjska w okresie 2014-2018
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.050 |
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