Truth and Lie in extremis: Holocaust Literature (Jakob the Liar, The Final Journey)
Bibliographic record
Abstract
Fiction is a curious term. Among those who engage in the study of literature professionally it holds an elevated status as a literary form that bears a decided epistemological thrust — primarily in the direction of providing an enhanced understanding of human behavior and human relations. Within social institutions, the legal apparatus especially, the notion of fiction enjoys a less acclaimed status. In the world at large fiction is nearly synonymous with the concept of falsehood. Within fiction itself, as a form of imaginative writing, subordinate fictions are commonly incorporated within the greater fiction. These may be either true or false, depending upon the tale itself; that is, fictions or outright lies are told against a background of ostensible truth, there being of course certain details that are taken as truthful within the fictive context, if not in reality. The notion of fiction embedded within fiction, which plays a considerable part in this essay, may be understood in at least two ways. First, independent stories may be embedded within the greater story, as an adventure is related as either a digression (of great interest but little relevance, as in Diderot or Gogol) or as an integral part of the work as a whole (as in Fielding or Dostoevsky), where it serves an explanatory role. Second, what is told may be simply and entirely false. Fiction, after all, necessarily contains aberrations and disfigurations of the truth in the form of false leads or
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".