“Always One Step Ahead of the Germans…”: Escapes of Polish Jews from France to Spain During the Second World War
Bibliographic record
Abstract
Abstract This article examines the flight of Polish Jews from France to Spain in the last months of 1942. It draws on refugee files gathered in the Spanish archives and autobiographical accounts created by the survivors to explore three aspects leading to their illegal escape across the Pyrenees: first, the antisemitic persecution that these Polish Jews experienced in France; second, their strategies and decision-making process leading to eventual passage to Spain; and third, the various ways in which the refugees prepared for this endeavor. For most Polish Jews, the illegal border crossing to Spain was an option of last resort used only when they felt that remaining in France, even in hiding, put their lives in imminent danger. Although they had heard of or experienced firsthand various anti-Jewish measures introduced in France throughout the war, it was not until the latter half of 1942, after massive roundups and the full German occupation of France, that they eventually decided in favor of a clandestine escape to Spain. Even so, their flight was not spontaneous but well-thought-out and carefully planned. By analyzing Polish Jews’ decision to flee from France, this article sheds light on the patterns of behavior that Polish and other Eastern European Jews shared in the face of antisemitic persecution in Western Europe throughout the war.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".