Censorship in Translation: The Dynamics of Non-, Partial and Full Translations in the Chinese Context
Bibliographic record
Abstract
This research focuses upon how the translation of certain types of literature in China evolved historically: from ‘non-translations’ (i.e., ‘translations’ unmade as well as made and yet strictly forbidden under given censorship conditions) to ‘partial’ or ‘full/near-full’ translations set against the backdrop of changing practices required by the country’s censorship policies. My analysis begins with an overview of the multi-faceted interface between censorship and translation, followed by the conceptualization of a typology of translations under censorship. This initial discussion, in turn, allows me to resituate specific translations, including the once absented translations of earlier times (i.e., prior to the 1949 Revolution or prior to the Cultural Revolution), which were initially taken at face value as ‘non-translations’ and yet which, later on, became ‘partial’ or ‘full/near-full’ translations under the country’s subsequently more relaxed censorial operations. I attempt to illustrate such shifts by means of in-depth discussion of the dynamic nature of translational commitment in connection with the change-resistant properties and evolving priorities of censorship. In illustrating my arguments, I will draw specific examples from case studies of three well-known censorship-affected translations – i.e., On China (Kissinger 2011), Lolita (Nabokov 1991) and The Good Earth (Buck 1960), which, I argue, epitomise the shifting degrees of translational commitment (‘non-,’ ‘partial’ and ‘full/near-full’) as they occurred in the Chinese context.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".