Bibliographic record
Abstract
The following text is a translation into Hungarian of two excerpts from Never, Again. Never, Again is a novel set in 1956 by Endre Farkas, a Hungarian-born Canadian writer whose “genre-fluid” practice encompasses poetry, performance, drama, videopoetry, and fiction. The novel emerges from a long creative continuum beginning with the chapbook Szerbusz (1974) and extending through performance (Face-Off/Mise au Jeu, 1980), poetry (Surviving Words, 1994), and drama (Surviving Wor(l)ds, 1999). Collectively, these works engage the author’s Hungarian experiences and his family’s Holocaust legacy, ultimately coalescing into Never, Again (2016), which was written over a four-year timespan. The first excerpt is the opening scene of the novel. A narrative overture, it is framed as a bedtime story told by a father, in which his son, Tomi Wolfstein, is the central character. This bedtime ycontains all the elements of what is to come. Flashbacks to the parents’ Holocaust experiences intersect with Tomi’s innocent worldview, creating a layered narrative that juxtaposes childhood innocence with historical atrocity. The second excerpt, positioned midway through the novel, marks a pivotal loss of innocence. In this moment, Tomi is betrayed by a trusted figure, exposing the pervasiveness of antisemitism and the moral duplicity embedded within Hungarian society. This personal betrayal mirrors broader historical continuities, revealing how collective trauma permeates intimate relationships. Together, these excerpts illustrate how Never, Again, through a child’s adventure, explores innocence, loss of innocence, and the emergence of a new self.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.019 |
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".