“Were I not here to record it, there would be no trace”: Chava Rosenfarb's “In the Boxcar” and Patrick Modiano's Dora Bruder
Bibliographic record
Abstract
Patrick Modiano’s Dora Bruder and Chava Rosenfarb’s “In the Boxcar” – an excerpt from the as-yet not fully translated novel Letters to Abrasha – rely on original and creative methods in their responses to events and memory associated with the Holocaust. In contrast with these works, this article also considers the approach taken in Michal Glowinski’s memoir The Black Seasons, as well as in Barbara Engelking and Jacek Leociak’s The Warsaw Ghetto: A Guide to the Perished City. These texts convey, in a new light, pre-war and wartime sites: Paris, Auschwitz, Lodz, Warsaw, and the ghettos installed by the Germans in the latter two cities. Dora Bruder de Patrick Modiano et «In the Boxcar» de Chava Rosenfarb - un extrait du roman Letters to Abrasha, qui n’a pas encore été entièrement traduit - s’appuient sur des méthodes originales et créatives dans leurs réponses aux événements et à la mémoire associés à l’Holocauste. En contraste avec ces oeuvres, cet article examine également l’approche adoptée dans les mémoires de Michal Glowinski, The Black Seasons, ainsi que dans The Warsaw Ghetto : A Guide to the Perished City de Barbara Engelking et Jacek Leociak. Ces textes présentent, sous un jour nouveau, des sites d’avant-guerre et de guerre: Paris, Auschwitz, Lodz, Varsovie, et les ghettos installés par les Allemands dans ces deux dernières villes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".