Bibliographic record
Abstract
This paper outlines the processes of censorship affecting translation under Nazi rule. Despite a markedly suspicious attitude towards translated fiction, the Nazi regime did not simply eliminate it. In fact, far from collapsing in 1933, the publication of translated fiction actually increased, both in absolute terms and as a proportion of all fiction, until the outbreak of war. However, if in purely quantitative terms translation flourished, the figures mask deep qualitative shifts: Jewish or anti-Nazi authors, translators and publishers disappeared; safe-selling genres came to dominate the market; and source-language preferences changed. These shifts were clearly the outcome of aggressive state measures, both classic “negative” censorship—the banning of literary producers and products or the imposition of “voluntary” self-regulation—and the energetic promotion of approved forms of translation. At the same time, more detailed study suggests that even for non-approved forms, the influence of state control was not always so clear-cut. In the case of the translated detective fiction of the time, censorship in translation was an amalgam of state intervention, pre-emptive filtering, selective readings of the source genre’s ambivalences, and the “normal” pressures of the book market. Even in this totalitarian context of extreme literary control, it remains difficult to define the borders of “translation censorship” as such.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".