Lolita’s Love Affair with the English Language: Heterolingualism and Voice in Translation
Bibliographic record
Abstract
This article is a first-person account of the translation of Lolita into Portuguese dealing primarily with the question of how to treat English as a source language that should be replaced by the translating language. The novel foregrounds the narrator’s stridency as a non-“native illusionist” (Nabokov 1955/1991: 317), along with a heterolingual bend, presenting remarkable challenges for translation: how to represent the geopolitics of linguistic hybridity in the TT and how to maintain the ambiguity of alignments between (implied) reader(s), author(s) and competing instances of narratorial authority, including the “fictional translator” (Klinger 2015: 16). Selective non-translation is suggested as an option for addressing linguistic hybridity through which, in this context, the “differential voice(s)” (Hermans 2007; Suchet: 2013) might foreground linguistic (and hence cultural/ideological) difference and deviation. The adherence to a strategy of “overt translation” (House 2001) is not intended to break the “translator’s pact” (Alvstad: 2014); it refuses, however, the convention of transparency as one of its tenets. It also shifts the focus from phonocentric authority to a polyphonous palimpsest and an archaeology of language(s) – not an entrenched foreignization, but an availability for “other-languagedness” (Bakhtin: 1981).
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".