Development Trends of Ukrainian Studies Centers in the USA and Canada and Future Horisms of their Activities
Bibliographic record
Abstract
Relevance of the research. The modern development of Ukrainian Studies remains largely focused on the study and popularization of knowledge about Ukraine and Ukrainian identity within the country itself, although it partly continues to rely on the inertia of pre-war achievements. However, after the Revolution of Dignity, the annexation of Crimea, the war in Donbas, and especially as a result of Russia's full-scale invasion on February 24, 2022, geopolitical circumstances have undergone dramatic changes. The courageous resistance of the Ukrainian people, the solidarity of Western countries in supporting Ukraine, and the large-scale acceptance of refugees in many countries of the world have awakened significant global interest in Ukraine. Over the past decades, Ukrainian Studies have often been lost in the shadow of Slavic Studies or Eastern European Studies, where Russian studies have dominated. However, Ukraine is now paying increasing attention to the development of Ukrainian studies abroad, treating it as a powerful tool of cultural diplomacy and foreign policy influence. Spreading knowledge about Ukraine and consolidating the international community around this topic have become key priorities. The purpose of the article is an analysis of the activities of major scientific centres in the USA and Canada in the field of Ukrainian Studies, the identification of development trends and the most relevant topics, and promising areas of future Ukrainian Studies in these countries. Conclusions. Ukrainian Studies centres in the USA and Canada currently cooperate with other institutions in Ukraine and the world, which contributes to the development of Ukrainian Studies, their integration into the global academic environment, a deeper understanding of Ukraine, and the dissemination of knowledge about it at the international level, which is of particular importance in the context of the Russian-Ukrainian war and modern geopolitical challenges. Having analyzed the topics of scientific research of the main Ukrainian centres in these countries in recent years, we have concluded that the priority areas of Ukrainian Studies in these countries are currently: the Holodomor in Ukraine in 1923-1933; problems of modern Ukraine, in particular its internally displaced population; oral history of the modern Russian-Ukrainian war; problems of reintegration of temporarily occupied Ukrainian territories; future reconstruction of Ukraine; Russia's environmental crimes during the current war; the imperial experience of Ukraine of the 17th–20th centuries; reasons and consequences of Euromaidan and the Revolution of Dignity of 2013 – 2014; linguistics, literary, church and religious studies, etc. In the future, scholars need to prepare original scientific research on the above-mentioned Ukrainian Studies issues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".