A. Context The greatest mass-arrest of Jews ever carried out on French soil is known as the Vél dhiv
Bibliographic record
Abstract
Round-up. It involved 13 000 victims from Paris and its suburbs. Over slightly more than two days, the Round-up involved nearly a third of the 42,000 Jews deported to death camps in Poland in 1942.The statistics for this terrible year account for over half of the total 76,000 Jewish deportations from France. Compared to the mass-arrests that had previously taken place in Paris on May 14, August 20-23 and December 12, this event is particular for a number of reasons, foremost being its scale. Because they had not developed the reflex of hiding, women and children were this time involved. The action was part of the vast deportation plan of European Jews, devised by the Nazis at the Wannsee Conference in January 1942. The Vél dhiv Round-up was a concrete case of execution of the Final Solution. The event also gave the government of Pierre Laval the opportunity to implement French sovereignty. The Vichy Armistice Convention of June 22, 1940, had provided for French sovereignty over its entire territory, but this principle was subsequently violated. René Bousquet, Vichy Secretary General of Police, then led new negotiations with General Carl-Albrecht Oberg. The nomination of these two individuals to their positions represents a landmark event. Bousquet had occupied his position since Lavals return to power in April. On March 9, 1942, Hitler had nominated Oberg for the position as Supreme Chief of the SS and of the German Police Military
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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 | 0.015 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".