The Pazifizierungsaktion as a Catalyst of Anti-Jewish Violence. A Study in the Social Dynamics of Fear
Bibliographic record
Abstract
Recently the historian Christoph Dieckmann has pointed to what he regards as an ongoing weakness in scholarship on the Holocaust. While historians have made tremendous advances in understanding and reconstructing the Jewish experience of genocide, he states, the same cannot be said for their approach to the non-Jewish world. This observation may serve as a fitting point of departure into the subject examined here: peasant violence towards Jews who sought shelter in the Polish countryside between 1942 and 1945. In Polish historiography, there have been several publications in the last 15 years dealing with Polish society and the Holocaust, and these have done a great deal to bring the perspective of the Polish-Jewish victim into the national consciousness. A dominant motif in this reappraisal has been a stress on the experience of Jewish ‘fear’ during and after the war, a trend recently modified by the historian Marcin Zaremba, who has used an umbrella concept, the ‘great fear’, to integrate the social anxieties of various ethnic and social groups in the immediate post-war period in Poland. Drawing from a comprehensive set of sources that survived for the vincity of Radomyśl Wielki, this article will reconstruct the context of ‘Polish’fear in more depth and track its fluctuations within a broader canvas of ‘social processes and social dynamics’. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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 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".