Bibliographic record
Abstract
A military report of September 1919 singled out Polish troops from the formerly Prussian province of Poznania as particularly abusive of, and prejudiced against, Belarusian Jews. This appears to have been a rather unusual case of German anti-Semitism in its Polish version. The Poznanians’ prejudice against Eastern Jews, so characteristic of German anti-Semitism, was exacerbated by their hostility against Poznanian Jews, with whom they had been in longstanding conflict. Experiencing a culture clash upon entering the settlements of Eastern Jews, they regarded their inhabitants not only as very strange and unfamiliar but also as far less civilized and even more Jewish than their Poznanian coreligionists. This attitude was compounded by the Poznanians’ twofold sense of superiority. First, Poznania was much more developed and contained a much smaller proportion of Jews than did Congress Poland, Galicia, and especially Belarus. Second, the Poznanians considered themselves the best unit of the Polish army and therefore looked down upon units from Congress Poland and Galicia, and especially on their officer corps, which they considered “Jew-ridden.” Many of these prejudices were shared by the Poznanian officer corps whose members, in any event, were reluctant to punish their men for anti-Jewish excesses because of their own sense of insecurity. As a result, the Poznanians were much more likely than any other Polish troops to abuse Belarusian Jews.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".