Bibliographic record
Abstract
Uri Orlev, the winner of the 1996 Hans Christian Anderson Award, is the most widely known Israeli author of children's books. Eight of his children's books have been translated into English, the largest number of any Israeli author who writes for children: six deal with Holocaust themes while two are contemporary picture books. To date, Orlev has written thirty books for children and three for adults in Hebrew. Like the tip of an iceberg, slightly over a quarter of Orlev's work is visible to the English reading public. Why were certain books chosen for translation? Are the untranslated texts inferior to those that have been translated, or are non-literary criteria at work? Does the choice to translate a text into English reflect something intrinsic in the text, or in the society that chooses to translate or reject a particular text? This paper examines Orlev's untranslated works, particularly the works of fantasy, while attempting to find answers to these questions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".