Dirty pretty language: translation and the borders of English
Bibliographic record
Abstract
This article analyses the politics of English, and translation into Englishness, in the film Dirty Pretty Things (Frears). With a celebrated multilingual cast, some of whom did not speak much English, the film nevertheless unfolds in English as it follows migrant characters living illegally and on the margins in London. We take up the filmic representation of migrants in the “compromised, impure and internally divided” border spaces of Britain (Gibson 694) as one of translation into the imagined nation (Anderson). Dirty Pretty Things might seem in its style to be a kind of multicultural “foreignized translation” which reflects a heteropoetics of difference (Venuti); instead, we argue that Dirty Pretty Things, through its performance of the labour of learning and speaking English, strong accents, and cultural allusions, is a kind of domesticated translation (Venuti) that homogenises cultural difference into a literary, mythological English and Englishness. Prompted by new moral panics over immigration and recent UK policies that heap further requirements on migrants to speak English in order to belong to “One Nation Britain” (Cameron), we argue that the film offers insights into how the politics of British national belonging continue to be defined by conformity to a type of deserving subject, one who labours to learn English and to translate herself into narrow, recognizably English cultural forms. By attending to the subtleties of language in the film, we trace the pressure on migrants to translate themselves into the linguistic and mythological moulds of their new host society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".