Constructive Efforts: The American Red Cross and YMCA in Revolutionary and Civil War Russia, 1917â24
Bibliographic record
Abstract
This dissertation is about American Red Cross and YMCA work in revolutionary and civil war Russia. It focuses on the most significant phases of these organizations’ efforts in terms of the numbers of personnel involved and the funds expended: Moscow and Petrograd, 1917–18; northern Russia during the Allied military intervention, 1918–19; and Siberia and the Russian Far East, from 1918 through the early 1920s. By drawing on dozens of often underused archival collections this study is able to discuss these “constructive efforts” in much fuller detail than have existing works. \nThe activities of the Americans who worked in Russia, rather than those who made policy from afar, are of primary interest. The concern here, beyond the what, where, and who, is why: Why did American relief or social service work occur? The answers, of which there are several, include a desire to provide assistance to suffering populations. But the humanitarian impulse was often not the one that carried the day when decisions about policy and practice were taken. Military concerns were important, especially while the Great War still raged on the western front, and while Allied and American soldiers fought Russian Bolsheviks. American relief workers also saw themselves as contributing directly to relations between Russia and Russians on the one hand, and the United States, the Allies, and the American people on the other. They were moved to carry out their work because they saw the importance of it for the present and future of relations between the two countries. Americans in Russia also took advantage of the presence of soldiers, civilian refugees, and former prisoners of war from a variety of European countries to spread the good word about all things American. Ultimately, Americans viewed revolutionary Russia through the lens of modernization. With American help, the future could be bright. With the right leadership in place to oversee their education, honest, hardworking, and intellectually curious peasants (as they were described by contemporary observers) could be turned into modern citizens. The Russian project failed to achieve its promise, but for a time Americans retained their optimism about Russia’s future.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".