When Literary Censorship Is Not Strictly Enforced, Self-Censorship Rushes In
Bibliographic record
Abstract
Understanding literary translation as part of a power game has led to renewed interest in issues of censorship in translation. In an effort to untangle the intricate relations between formal law and (internalized) norms, this essay will focus on voluntary or self-imposed censorship in areas where formal censorship (i.e., legislated law, religious law) is not strictly enforced. It will first briefly describe certain aspects of formal censorship in Israel, then present cases in which the borderline between formal censorship and self-censorship seems blurred. Two particular cases will be examined: one has to do with the attitude of translators towards the use of the words “pig and pork,” the other with the Committee established by the Ministry of Education in the 1960s to censor obscenity in literature. These cases will help shed light on the deep roots of self-censorship mechanisms and the reduced need for formal censorship when subordinate groups or individuals feel that working with the consensus is more beneficial than working against it. The case of a book banned in the Orthodox community—and therefore pre-censored for translation—will examine another aspect of censorship, that of the corrective measures applied when voluntary self-censorship is not exercised.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".