The German concept of “self-cleansing actions” (Selbstreinigungsaktionen) – formulation, implementation, oblivion, and suppression
Bibliographic record
Abstract
Over a quarter of a century has passed since film documentary director Agnieszka Arnold discovered previously unknown information on the “self-cleansing action” in Jedwabne. The brief period of interest taken in this subject by the media and historians did not bring a thorough explanation of the “Jedwabne complex”. We still know little of how the Germans and Austrians forming part of Einsatzgruppen instigated and implemented, or instead inspired, genocidal Selbstrenigungsaktionen taking place in the rear area of all German army groups (Heeresgruppen). The Romanians adopted a slightly different strategy, although it, too, resulted in the extermination of the Jewish population in the territories recovered after their seizure by the Soviets in 1940. In popular understanding of the issue, the so-called pogroms (although this is not a correct term) occurred only in Podlasie/Podlachia/Podlachien region, Kovno/Kauen/Kaunas, and Lvov/Lemberg/Lviv. In reality, their scope spanned the entirety of the “Bloodlands”, from the General Government to the Caucasus. It is important to note that the term Selbstrenigungsaktionen is practically absent from scholarly discourse (suffice it to type it in an Internet search engine). It is worrying that this genocidal episode of the Shoah goes nearly unstudied – of course, as part of international research initiatives free from accusations of biased exploration.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".