American Soy in the USSR: the Transfer of the U.S. Agricultural Practices into the Soviet Economy During Détente
Bibliographic record
Abstract
This article, based on previously unpublished documents from the Russian State Archive of Contemporary History (RGANI), examines the transfer of American ideas and practices to the USSR. The case study focuses on an expert memorandum submitted by the Institute for the U.S. and Canadian Studies of the Academy of Sciences of the USSR to the Central Committee of the Communist Party of the Soviet Union (CPSU) in 1976, addressing issues related to the production and use of soybeans in the Soviet Union. The research follows a constructivist approach, highlighting how the image of the United States as a technological leader became more prominent during Soviet economic reforms, and how American modernization practices were seen as a model to follow. The use of anthropological methods provides a detailed look on how Soviet officials viewed and implemented American ideas within their own economic system. Using cultural transfer theory and tracking the “life” of the memorandum within soviet bureaucratic practices, the author explores why some expert recommendations were set aside, while others were adopted. The study concludes that the success of American ideas, such as the “soy project”, depended on the support of influential officials and the ability to adapt these ideas within smaller, manageable modernization efforts that didn't require major changes to the Soviet economic structure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".