The Russian-Ukrainian War and the International Community: a monograph. Edited by V. Smolii. Kyiv: Institute of History of Ukraine of the National Academy of Sciences of Ukraine, 2023. 264 p.
Bibliographic record
Abstract
The monograph analyses the dynamics of changes in the policies of European, North and South American, Asian and African countries regarding the Russian-Ukrainian war of 2014-2023. It is shown how the policies of the states of ‘old’ and ‘new’ Europe have been transformed in relation to the at first ‘hybrid’ war, and later to Russia's large-scale aggression against Ukraine using all conventional weapons. The dynamics of the United States and Canada's policy towards Russia's war against Ukraine is highlighted. The peculiarities of the attitude of China, India, Argentina, Brazil, South Africa and some other countries of Africa, Asia and South America to this war in the centre of Europe are considered. The reasons and consequences of the policy of ‘neutrality’ of individual countries in relation to the Russian armed aggression against Ukraine are clarified. The evolution of the policy of the European Union, NATO, OSCE, and the UN on the Russian-Ukrainian war and the activities of Ukrainian diplomacy in defending Ukraine's sovereignty and territorial integrity are studied. Recommendations are given on how to improve Ukraine's diplomacy of peace and international security and curb Russian armed aggression, which is a threat to the entire democratic world. For international experts and a wide range of readers, all those interested in contemporary international relations and Ukraine's place and role in their transformation in the context of the Russian-Ukrainian war of 2014-2023
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".