Bibliographic record
Abstract
Matthew Payne. Stalin's Railroad: Turksib and the Building of Socialism. Pittsburgh: University of Pittsburgh Press, 2001. x, 384pp. Illustrations. Notes. Bibliography. Index. $37.00, cloth.Stalin's Railroad is a thoroughly researched, well-presented case study of one of the largest construction projects of the First Five-Year Plan era. The Turkestano-Siberian Railway (Turksib) was begun in December 1926 and completed ahead of schedule in January 1931. Matthew Payne's volume shows how this project was emblematic of Soviet efforts to create a modern society populated by modern people. He focuses on two main groups, workers and engineers, and by tracing their experiences during the construction process, he is able to demonstrate how the state tried to control society and to what extent it was successful in doing so. An interesting additional element to this work, and one that sets it apart from earlier local studies, is its consideration of nationality, specifically the difficulties faced by Kazakh workers who were trapped between a state that encouraged them to think of themselves as potential proletarians and European workers who were unwilling to see them as equals.Chapter One lays the foundation of the book by exploring the disagreements that affected the project even before a single kilometre of track was laid. Payne's discussion of the squabbles between Caspian, Narkomfin, and Narkomput' concerning the route of the railroad, as well as the resources that were needed to build it, suggest that an atmosphere of constant, intense bureaucratic rivalries dominated the economic decision-making process during these years. His study is also valuable because it considers the way in which local authorities lobbied to protect their interests. In particular, Payne notes how regional authorities were able to manipulate Moscow's sense of Turksib as a civilizing force in order to further their own economic development interests.The second chapter outlines the generational struggle that affected Turksib's engineers. Specialists who were trained in the pre-Revolutionary period were watched with suspicion by a new group of Red engineers. While the older specialists might have had greater technical knowledge, the younger Red engineers were better able to manipulate the ·, new Soviet reality. For instance, they gained favour by suggesting ways to cut costs and proposing cheaper routes-two attractive ideas to the central authorities in a time of fiscal austerity but also ones that called into question the authority of the older generation of engineers. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".