Rósa Magnúsdóttir. Enemy Number One: The United States of America in Soviet Ideology and Propaganda, 1945–1959.
Bibliographic record
Abstract
In September of 1999, in Moscow, after an international conference at the Institute of World History (Russian Academy of Sciences), I had a long conversation with my former mentor Nikolai Bolkhovitinov about a new trend in the historiography of Russian-American relations, related to a historical analysis of production, dissemination, and consumption of the images of “alien Others,” which affected both politicians and ordinary audience in Russia or the USSR and the United States, especially during the Cold War. Bolkhovitinov, who was a head of the sector of the history of the U.S. and Canada of the Institute of World History in those days, introduced me to the research of the young (then) scholars I. Kurilla, V. Zhuravleva, and N. I. Nikolaeva, who explored a role of those images in shaping of the Russian-American relations during various periods of history. He especially praised a pioneering research work of Nikolaeva about the early stages of the Cold War, which eventually Bolkhovitinov published in Amerikanskii Ezhegodnik in 2002. The trend, we discussed with Bolkhovitinov in 1999, now became the most popular tendency in a historiography of the Cold War in both post-Soviet (Russian and Ukrainian) and Western studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".