PARTICIPANTS OF SOLVING MILITARY CONFLICT IN THE SOUTHERN EAST OF UKRAINE
Bibliographic record
Abstract
The article is devoted to analysis of steps and activities of different actors of international relations as for solving military conflict in Donbass. It was analyzed diplomatic steps done by such countries as France, Canada, Germany, the USA in order to solve the conflict as quickly as possible. It was shown that only France and Germany were very active in the peacemaking process, whereas the rest of the countries helped Ukraine in another way. Canada provided Ukraine with military instructors who consulted and trained Ukrainian soldiers. Together with Norway Canada tried to solve the problems of humanitarian sphere. It was analyzed that these countries helped civilians to overcome the problems of the hostilities. It was proved that Lithuania was the only country that provided Ukraine with lethal weaponry, mainly of Soviet production. It was done due to the fact that Russia and Lithuania have very tense relations and the latter wanted to help Ukraine cope with Russian policy. It is emphasized that France is willing to become very active at least in European region and became initiator of negotiations between Ukraine and Russia. Nowadays French president says that it is necessary to improve relations with Russia because it is impossible to create European security system without it. Germany’s behavior was thoroughly analyzed due to the fact that it is a leader of European Union and a lot depends on its position. The author showed attitude of German government towards Ukrainian conflict and proved that it has changed because of the Nord Stream – 2. Being interested in its building Germany has softened its position towards Russia and now we can see that it can be even pro-Russian than pro-Ukrainian.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".