From Spaghetti-O's to Osso Bucco: Francophone Translations of Suburban America
Bibliographic record
Abstract
The computational affordances of digital tools and methods have enabled new avenues of research in translation studies, allowing scholars to examine translation decisions at scale through the creation and analysis of parallel corpora. This paper will focus on multiple French translation of Ann M. Martin's series, The Baby-Sitters Club. This series, featuring a club of teenage girl entrepreneurs committed to providing quality childcare at affordable rates in the suburban Connecticut of the late 1980's through 1990's, is full of cultural references to US middle-class life at the time. Of the over 200 volumes published between 1986 and 2000, 85 books were translated for young readers in Quebec, between 1991-1996. Between 1990 and 1993, 22 books were translated into French in Belgium, and a publisher in France also translated 53 books between 1997 and 2003. This paper uses named-entity recognition (NER) and translation alignment to map the boundaries of localization translations, with an emphasis foods. We use the Bleualign sequence alignment tool to align the three French translations and the English original. We have trained a SpaCy NER model to identify food in English, and will use the aligned corpus of translations to train a comparable model for French. By compiling lists of correspondences and divergences across the translations, we will be able to more clearly articulate what kinds of places and foods challenged the translators' imaginations to a point where they had to be imported directly as "foreign" elements. This paper will contribute to the broader field of DH by offering annotated children's literature as a ground truth source for NLP model training (in a manner compatible with current copyright law in the US, see Bamman et al. 2019), by serving as a case study for the use of DH in translation studies, and as a step towards further research on the effects of localization in the global flow of popular culture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".