In Search of “Good Russians”: Ukrainian-Russian Encounters in the United States During the First Cold War
Bibliographic record
Abstract
The article is devoted to the topic of Ukrainian-Russian intellectual encounters in exile during the Cold War. The author focuses on Ukraine’s and Russia’s mutual representations in historical narratives in connection with their respective discourses of national identity. The article also describes sporadic attempts at establishing Ukrainian-Russian public dialogue in exile starting in the early 1960s. All of them were initiated and conducted by Ukrainian public activists and intellectuals. The author concludes that participants on both sides ascribed opposing meanings to historical terms. Russian authors, on the one hand, consistently used the modern designation “Ukrainian” as a synonym for “Little Russian,” which automatically situated Ukraine within the “pan-Russian” historical framework. Ukrainian historians, on the other hand, tried to reinterpret “Russian” as a modern national designation rather than an imperial one. Hence the Ukrainian-Russian dialogue had no chance of succeeding unless Russian participants agreed to rethink their discourse of national identity. It is no wonder that many American observers remained confused about the nature of Ukrainian-Russian debates: to them, they looked like a dead-end situation. Thus, rather than trying to find alternative interpretations of Ukrainian and Russian history, most Western specialists followed either one or the other respective national narrative.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".