The use of the topics and vocabulary of the Second World War in the Russian-Ukrainian conflict 2014-2020 (analysis of foreign historiography)
Bibliographic record
Abstract
The object of research is the latest English literature of scientists and analysts in Western Europe and America, devoted to the problems of the information war waged by the Kremlin authorities against Ukraine. It is the problems, events of the Second World War, in the Russian-speaking academic space and the media that are still referred to only as the Great Patriotic War that became the leitmotif of these actions. Moreover, the history of the war is used precisely speculatively, that is, a careful selection of facts and conclusions is carried out, which are adjusted to certain conceptual foundations. Investigated problem. As a result of a critical analysis of the works of Western authors, the problem posed in this work is solved: to show the objectivity and evidence of the narratives of foreign authors on the biased presentation of the topics and vocabulary of World War II about the events in the East of Ukraine in 2014-2020. Main scientific results. The main scientific result of the study is the conclusion that Western scientists are deeply immersed in the vicissitudes of the conflict, debunking the biased evidence of Russian experts, fascist tendencies are imposed on the basis of false accusations of Ukraine, although such processes have not been recorded by any foreign analysts and observers. One of the stereotypes of Russian propaganda is the reproach for the massive "banderization" of the consciousness of citizens, but at the same time the face and activities of the leader of the OUN (b) are deliberately distorted. Modern studios of historians, diplomats, analysts of Western Europe, the USA, Canada are convincingly criticizing the political and ideological measures of Moscow aimed at discrediting the Ukrainian people. It is emphasized that the process of "nationalization" of history in Ukraine, especially during the Second World War by the Russian establishment, was used in a fraudulent way to tarnish the past of Ukraine. It was noted that, despite all the efforts of the Russian information and propaganda machine, Moscow fails to achieve the desire for results. Scope of practical application. The research results, which are innovative in nature, can be used in both cognitive and educational values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".