‘“Un train peut en cacher un autre…”: Entretien avec Arnaud Rykner. Propos recueillis par Helena Duffy’
Bibliographic record
Abstract
The interview with Arnaud Rykner, French novelist, playwright, and Professor of French and Theatre Studies at the Sorbonne–Nouvelle in Paris, focuses on the author’s novel, Le Wagon (2010), recently translated into English by Sue Boswell as The Last Train(to be published in 2020 by Snuggly Books). Based on the experience of the deportation of the author’s great uncle as well as on archival sources, the novel retraces the journey of the train numbered 7907 from the transit camp of Compiègne to the concentration camp of Dachau in July 1944. The narrative describes the unhuman conditions in which the deportees travelled, and which consisted of excruciating heat, overcrowding, lack of food and drink, and the violence that unsurprisingly ensued in such circumstances. During the nearly three–day journey, a quarter of those who had originally boarded the train perished. In the interview with Helena Duffy, Rykner talks about the genesis of his book, the ethics of writing about the Holocaust in the post–witness era, and the phenomenon of the so–called ‘Jonathan Littell Generation’ with which he identifies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".