Gry w Holokaust. Yann Martel: <i>Beatrycze i Wergili</i>. Przeł. Andrzej Szulc. Warszawa, Wydawnictwo Albatros A. Kuryłowicz, 2010, ss. 240
Bibliographic record
Abstract
The Holocaust Games. Yann Martel: Beatrycze i Wergili. Przeł. Andrzej Szulc. Warszawa, Wydawnictwo Albatros A. Kuryłowicz, 2010, ss. 240. In a review of Yann Martel’s novel Beatrice and Virgil, the work of the Canadian writer is presented through the pursuit of a new language of speaking about the Holocaust. This is a task which Martel as well as the protagonist of his novel, Henry, the author of a work about the Holocaust of the Jews, in which he futilely attempted to go beyond the sanctioned ways of writing about the Shoah, set themselves. In Beatrice and Virgil a crucial feature is the interpenetration of two orders – the problem of thematising the Shoah meets an animal theme, which is represented by Legend of St. Julian the Hospitaller, a story about an obsessive hunter, and through the art of preparing and stuffing the bodies of dead animals and transforming them into artistic exhibits. In Martel’s work, the master of taxidermy is also the author of a theatrical play about a sheass and an ape, Beatrice and Virgil, which became the victims of events which bear a striking similarity to the various scenes of the Shoah. The basic problem in the novel is not so much a problematisation of the Holocaust but the means of “linguifying” it and the position which may be asserted by the writers who present their Shoah‑related narrations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".