Winning Women's Hearts and Minds: Selling Cold War Culture in the U.S. and the U.S.S.R.
Bibliographic record
Abstract
As early as October 1945, the State Department commissioned a study of U.S.-Soviet relations by a leading American social scientist, Harold Lasswell. He concluded that America and the Soviet Union would confront each other on practically every major issue, which would likely result in a cultural armaments race in the form of scientific, artistic, and educational expenditures. The architects of the Cold War responded by creating new institutions, principally the U.S. Information Agency (Usia), Voice of America (Voa), and Radio Free Europe/Radio Liberty, and by developing different programs to showcase American life and values, including radio broadcasts in Russian and other languages of the Soviet Union, artistic and educational exchanges, and periodicals in Russian. From this rich smorgasbord Diana Cucuz has chosen to do an in-depth analysis of one publication, Amerika, a monthly magazine prepared by the Usia and distributed in the Soviet Union during the Cold War. Her central argument is that Amerika was geared toward Soviet women, who would be attracted to American life by seeing “images of supposedly happy and fulfilled American women as feminine housewives and mothers living under a US capitalistic consumer culture” (p. 4). These images, Cucuz contends, would over time encourage a desire for consumer goods that would ultimately undermine the Soviet regime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".