Bibliographic record
Abstract
On July 4, 1946 in the city of Kielce a Polish mob, a Polish police detachment, and a unit of the Polish Army murdered over forty Jewish survivors and injured dozens more.1 The pogrom shocked Polish and international public opinion. How could it happen, many asked, that only a year after the Holocaust, Jews had been killed again. Why did people of Kielce, who had witnessed the annihilation of the city’s Jewish community, murder some of the few surviving Jews, most of whom would have left Poland anyway? After seventy-three years of research represented in innumerable publications, we still wonder. Researchers have adopted various approaches. Most believe it necessary to study the Holocaust in Kielce to understand Polish-Jewish relations afterward. Sara Bender, a renowned Holocaust scholar and long-time professor of Jewish history at the University of Haifa, shares this conviction and devotes her book primarily to the Holocaust in the region. Her description of the murder of the Jews of Kielce by the Germans and their local helpers is so terrifying that writing a review of her text almost feels wrong. There is no doubt: thousands had been murdered in Kielce or sent from there to be murdered, and the details Bender provides highlight the magnitude of the crime.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".