Bibliographic record
Abstract
The article discusses the interaction between reading and translating, in the case of the first unabridged translation of Moby-Dick into French by Jean Giono, Lucien Jacques and Joan Smith, published by Gallimard in 1941. After a brief survey of the status of that translation—an important cultural landmark in France—the paper examines what the paratext (Giono’s diary, notes and letters) and the typescripts reveal about a seemingly paradoxical situation: Giono’s keen reading of Moby-Dick on the one hand and the simplification and clarification strategies adopted in the translation on the other hand. A selection of stylistic analyses illustrates both the choices made by the translators and the part played by each participant in the project. It appears that Giono did not necessarily misread Moby-Dick, underestimating its scope and significance. Instead, after reading the novel, he grew indifferent to its translation and concentrated his energy on his own writing in which he re-invested his reading experience. As to the other co-translators, Joan Smith provided a word-for-word translation of the text that made no attempt at interpreting the text, while Lucien Jacques strove to re-write Smith’s literal first draft, in spite of his difficult position as a non-reader (albeit an enthusiastic one) of Moby-Dick.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".