At the Borders Between Translation and Parody: Lydia Davis’s Story about Marie Curie
Bibliographic record
Abstract
Lydia Davis’s story “Marie Curie, So Honorable Woman” poses a number of questions related to its status. It is presented as a story, but it is constructed from translations of extracts of Françoise Giroud’sUne femme honorable, which Davis had previously translated asMarie Curie: A Life. This article analyses how the story questions the borders between translation and other forms of intertextual writing. First it analyses how the text was presented in its magazine publication inMcSweeney’s Quarterly Concernunder the title “Translation Exercise #1: Marie Curie, Honorable Woman.” It then discusses how Davis’s use of abridgement in this story and other stories is similar to translation before analysing the translations in the story, which exaggerate the interference from the source language. Along with the choice of extracts, this translation strategy suggests that the story is a parody. It follows the legal and literary definitions of the parody because it exhibits a critical distance from its source text. But it is parody of a text which is not well known in the target culture and so it is unlikely to be recognised as a parody by readers. As a text, “Marie Curie, So Honorable Woman” questions the relationship between translation and parody, but it also questions ideas about representation through its style and its relation to its source text.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".