Bibliographic record
Abstract
Before the archives containing files on Soviet security policy were declassified in the mid-1990s, authors who explored the conflict in the borderlands based their studies on the memoirs of Soviet partisans and nationalist guerrillas, propaganda pamphlets printed by the resistance and the Soviets, a few Soviet documents captured by Germans and nationalists, and reports of the German counterintelligence. None of these sources yielded much information on Soviet counterinsurgency, and most distorted other aspects of the conflict. The memoirs of Red partisans, sanitized by censors, had to fulfill the order of the Communist party to maintain the myth of an all-people's war against the German invaders. Their authors exaggerate the popular support partisans enjoyed; they rarely mention the nationalist opposition that in some regions was stronger than the German one, and if they do, they inaccurately portray the nationalists as mere German pawns. Since few former partisans survived until Perestroika , no important memoirs emerged when censorship ended in the former Soviet republics. Although some memoirs published in the Soviet Union are more honest than others, they have limited value as primary sources. The former nationalist guerrillas who escaped to the West had no censors, yet, not unlike Red partisans, they pursued a political agenda. They typically misrepresented Soviet policy, oversimplified the social tensions in the borderlands, and downplayed the radicalism of their movements.
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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 | 0.015 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.029 | 0.038 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.501 | 0.371 |
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".