<i>But I Live: Three Stories of Child Survivors of the Holocaust</i>. Charlotte Schallié
Bibliographic record
Abstract
“Death was among us every day,” explains Emmie Arbel, a child survivor of Ravensbrück and Bergen-Belsen (p. 95); “People were dying…. And that was the life, that was the only thing I knew. We knew that every day, we can die. You live with it” (p. 105). Arbel’s starkly candid account of her experience in the concentration camps is one of four testimonies given by child survivors who lived through the Holocaust, and whose firsthand accounts are at the heart of this remarkable collection of stories. The presence of death set against the will to live frames the narratives of the child survivors collected in this volume and creates the thematic tension that threads its way through each story. These stunning testimonies also provide an opening for a panoramic view of the widescale devastation of the Holocaust. As individual narratives of ordinary children caught in the horrors of the machinery of genocide, they reflect the enormity of the Nazi assault on humanity. But I Live is a uniquely conceived and structured work that explores new possibilities for Holocaust representation at a moment in history that will see the end of direct survivor testimony. It is distinguished by its multigenre, polyphonic layering of perspectives and forms of representation, organizing a deeply engaging dialogue among survivors, graphic artists, and scholars, who, in concert, arouse, mediate, and reckon with the traumatic past.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".