Bibliographic record
Abstract
The mechanisms and the chronology of the great crimes committed by totalitarian regimes are now well documented. While they may explain the mechanics of these events, they do not always explain why they transpired. The implementation of Stalin’s policy of collectivization and de-kulakization relied on dissimulation. Moreover, the pace of collectivization was justified by external threats, initially from Great Britain and Poland, and later extending to Japan. This made possible the branding of any political adversary as a traitor. As long as Stalin faced organized political opposition, he was unable to launch any maximal policies. After the defeat of Trotsky in December 1927 he was able to create crisis situations that ultimately furthered his own power. The offensive he unleashed against the peasants became a means of reinforcing his increasing dictatorship. The collectivization campaign employed the rational argument that the backward countryside needs to modernize production. Its ultimate aim, however, was the crushing of an independent peasantry. There are enlightening comparisons that can be made between collectivization in China and the USSR, which are explored in this essay. The resistance to collectivization was particularly strong amongst Ukrainians. Stalin, who had long regarded the national question as inseparable from the peasant question, deliberately chose mass starvation to break resistance to his will. The history of these events was for a long time shrouded in great secrecy until it began being discussed by Western scholars, becoming a matter of considerable debate between the “totalitarian” and “revisionist” schools of Soviet historiography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".