The Translation of Sex-Related Language: The Danger(s) of Self-Censorship(s)1
Bibliographic record
Abstract
While censorship is an external constraint on what we can publish or (re)write, self-censorship is an individual ethical struggle between self and context. In all historical circumstances, translators tend to produce rewritings which are ‘acceptable’ from both social and personal perspectives. The translation of swearwords and sex-related language is a case in point, which very often depends on historical and political circumstances, and is also an area of personal struggle, of ethical/moral dissent, of religious/ideological controversies. In this paper we analyse the translation of the lexeme fuck into Spanish and Catalan. We have chosen two novels by Helen Fielding— Bridget Jones’s Diary (1996) and Bridget Jones: The Edge of Reason (1999)—and the translations into the languages mentioned. Fielding’s acclaimed first novel has given rise to a distinctive genre of popular fiction ( chick lit ), which is mainly addressed to young cosmopolitan women and deals unconventionally with love and sex(uality). Historically, sex-related language has been a highly sensitive area; if today, in Western countries at least, we cannot defend any form of public censorship, what we cannot prevent (nor probably should we) is a certain degree of self-censorship, along the lines of an individual ethics and attitude towards religion, sex(uality), notions of (im)politeness or (in)decency, etc. Translating is always a struggle to reach a compromise between one’s ethics and society’s multiple constraints—and nowhere can we see this more clearly than in the rewriting(s) of sex-related language.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".