To Make a Village Soviet: Jehovah’s Witnesses and the Transformation in a Postwar Ukrainian Borderland. By Emily B. Baran
Bibliographic record
Abstract
This book recounts the complex story of how Sovietization happened to a remote village on the Soviet-Romanian border in the aftermath of World War II. Taking as her case study the trial of seven Jehovah’s Witness men from the Romanian-speaking village of Bila Tserkva in 1949, Emily Baran unpacks in considerable depth the locally experienced implications of state policies and bureaucratic processes on the Jehovah’s Witness community that ultimately led to the trial and sentencing of seven men on charges of anti-Soviet activity. Whereas Baran’s previous book Dissent at the Margins, published in 2014, placed a firm focus on resistance and the agency of believers, To Make a Village Soviet shifts emphasis to the significance of the participation of local citizens—the neighbors, colleagues, and families of the accused—for understanding the painful process of Sovietization. The choice of Bila Tserkva as her case study allows Baran to introduce a very specific combination of problems faced by Soviet authorities in Transcarpathia, who as well as grappling with a lack of knowledge and understanding of Jehovah’s Witnesses (of which the Soviet state had little prior experience), also encountered the unique challenge of integrating a border territory that had never previously been part of the Soviet Union. As Baran argues, not only did the Soviet state not appreciate who they were dealing with concerning the Jehovah’s Witnesses, the Witnesses had no idea what to expect from the Soviet state.
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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 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".