Bibliographic record
Abstract
Nicolas Werth. Cannibal Island: Death in a Siberian Gulag. Trans. Steven Rendali. Human Rights and Crimes Against Humanity. Princeton: Princeton University Press, 2007. xxii, 226 pp. $24.95, cloth.In May 1933, some 6,000 declasse were dumped on Nazino, an inhospitable island in the middle of the Ob River. As part of a massive, albeit ultimately short-lived campaign to resettle approximately 2 million social undesirables from major urban centres, these labour colonists had been sent to Western Siberia to establish new communities like those set up during earlier kulak deportations. Poorly guarded, dreadfully under provisioned, and woefully unequipped to deal with the situation in which these prisoners found themselves, a tragedy quickly unfolded. Over the course of a few weeks, at least 2,000 thed, and hundreds more disappeared into the taiga. Over and above the high rate of mortality among the colonists, what made this incident particularly shocking were dozens of reported cases of cannibalism and necrophagy on the island. Such acts, driven by a potent combination of sadistic cruelty practised by criminal elements among the deportees and desperation created by starvation conditions, make this one of the most gruesome chapters in Soviet history. This Nazino affair, subsequently discussed by Stalin and the Politburo and investigated by a high-level Party commission, is me subject of a remarkable new work of microhistory by Nicolas Werth.Werth's reconstruction of the events in question, based on a wealth of sources, including documents from the Presidential Archive of the Russian Federation and the central FSB archive, is impressive in itself. Werth uses these sources judiciously, presenting long quotations that effectively illuminate the personalities who participated in the tragedy, from the perspectives of both victims and perpetrators. Excerpts from profiles of the victims, many of whom were arbitrarily swept up in urban cleansing operations and deported, have a particularly powerful effect on the reader. But the value of the book lies not only in its use of sources, but also in the narrative that Werth constructs to explain both the origins and implications of the Nazino affair. Narrowing his focus in each successive chapter, he explains how various state policies, practices, and decisions from the national to the local level led the events to unfold in the manner described in oral testimony and archival documents. In the process he explores such diverse elements of Soviet history as collectivization, passportization, centre-periphery relations and the number culture of Stalinist governance. …
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".