'Maman' or 'Mother': A Closer Look at Word Choice in Translations of Albert Camus’ L’Étranger
Bibliographic record
Abstract
When the topic of French literature comes up, Albert Camus’ L’Etranger is one of the first novels that comes to mind. As one of the most translated novels worldwide, L’Etranger has global reach and continues to be one of the most influential French novels on the absurd. In translation, one of the key issues has to do with the translator’s word and language choice. A controversial, and often discussed, example is the first sentence of the novel. The first sentence has the ability to shape, and even change, the reader’s perspective. By analyzing and comparing Camus’ original French text to Stuart Gilbert and Matthew Ward’s respective English translations, we see how Gilbert and Ward’s choice of “Mother” versus “Maman” has an affect on the reader’s perception of the main character Meursault. Inspired by Venuti’s idea of the translator’s invisibility, by bringing the focus to the translator’s decision, we better understand why each translator selected a different word in translation. Acknowledging Gilbert and Ward’s role as a translator within the text provides a more active and inclusive voice that justifies their decisions instead of ignoring them. The reader then understands how Meursault is read differently across the three versions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".