Bibliographic record
Abstract
REVIEWS 779 More broadly, The Jewish Revolution in Belorussia also provides a compelling argument that the global economic and political crisis of the late 1920s played no small role in shaping racialized conceptions of antisemitism. ‘Each episode of crisis,’ Sloin writes, ‘hypostasized a certain manifestation of “Jewishness” and took this manifestation to be a malignancy on the body politic’ (p. 243). Andrew Sloin’s attempt to chart how the Bolshevik movement restructured Jewish lives in Belorussia is an engaging and theoretically sophisticated study. The book makes a timely contribution to Soviet and Jewish history and modern European history, and deserves to be read widely. University of Illinois Eugene M. Avrutin Graziosi, Andrea and Sysyn, Frank (eds). Communism and Hunger: The Ukrainian, Chinese, Kazakh, and Soviet Famines in Comparative Perspective. Canadian Institute of Ukrainian Studies Press, Edmonton, AL and Toronto, ON, 2016. viii + 158 pp. Maps. Figures. Tables. Notes. Bibliographies. $24.95 (paperback). This book presents six of the papers from a conference on Communism and hunger held at the University of Toronto in 2014. Only a few of the papers have substantial academic value, while most of them have flaws that undermine their validity and novelty. The best contribution is the comparative study of famines in Central Asia by Niccolò Pianciola, which places the history of the Soviet Kazakh famine of 1931–33 in the context of Soviet and Chinese treatment of their spheres in Mongolia and of the effects of the Chinese Great Leap Famine [GLF] in Tibet and Central Asia in 1958–61. Using archival and other primary sources in Russian and Chinese, Pianciola shows that the Soviet government restrained collectivization and livestock procurement policies in Mongolia in the 1930s, unlike in Kazakhstan, and that China emulated the Soviet Virgin Lands programme in the 1950s. The paper by Xun Zhou on the GLF presents revealing examples of the extreme violence perpetrated by Chinese officials, based on her monograph and document collection on the GLF. Sarah Cameron’s brief paper on the Kazakh famine discusses some of the scholarly literature, showing the dispute over the use of the term ‘genocide’. All these articles have minor errors in their discussions of the USSR. Theotherarticlesinthebookareweaker.LucienBiancoarguesthattheUSSR and PRC faced similar problems of chronic famines and growing population that both exploited the rural economy to support industrialization; that both increased grain collections from the villages during the famine; and that both chose to supply urban populations at the expense of villagers. The two SEER, 95, 4, OCTOBER 2017 780 cases differed, according to Bianco, in that the Chinese regime disrupted the villages more than in the USSR with the people’s communes, that nationality was more important in the USSR, and that the Chinese famine lasted longer and was more lethal and inconsistent. Bianco also compares the roles of the ‘actors’ responsible for the famine, among whom he includes some of the peasants (p. 76), and the different responses of the victims to the famines. Bianco’s comparisons are not very profound and have errors. He asserts, for example, that neither the Soviet nor Chinese authorities reduced grain procurement quotas (p. 59), but then admits that the Soviet government not only reduced quotas but returned grain to the villages (p. 59), and that the Chinese authorities reduced quotas once they learned of the famine (p. 71). He asserts that the Soviets had ‘local famines’ in 1924 and 1926, but in fact there was no famine in 1926 because the USSR had a large harvest that year — Werth in his essay calls 1925–27 a ‘golden age’ (p. 11), but Bianco does not mention this. The 1924 famine was not local but affected Ukraine, the Volga and other regions, which Werth also acknowledges (p. 11). Bianco asserts that Stalin in 1928 ‘needed to feed 41 million more people than Tsar Nicholas’ in 1897, but the difference between the Soviet 1926 census (147 million) and the 1897 census (125 million) was 22 million. He also cites Wemheuer but omits his finding that environmental factors reduced the 1960 harvest that caused the peak of the famine (Felix Wemheuer, Famine Politics in Maoist China and the Soviet Union, New Haven, CT, 2014, pp. 12, 246). Nicholas...
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.468 | 0.456 |
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".