Traduire un féminisme ambivalent: l'exemple de "Difficult Women" de Roxane Gay suivi de la traduction "Des femmes compliquées"
Bibliographic record
Abstract
This master’s thesis has two components: the translation, from American English to Québécois French, of a selection of five short stories from Roxane Gay’s collection Difficult Women (2017), and a critical analysis related to the translation. The latter proposes to reflect on the specific considerations raised by the translation of literary works that illustrate a “meta-” (Saint-Martin, 1992) or a “bad” (Gay, 2014) feminism, the particularities of these texts, as well as the translation strategies that may be adopted or adapted in order to render them from one language to another. First, the ideological issues specific to any translation are outlined as well as the influence of hegemonic discourses – especially sexist ones – in language. I also highlight possible forms of resistance through (re)writing. Then, since women’s literary practices in recent decades seem more ambivalent in their identification with feminisms, the collection of short stories “Difficult Women” serves as a case study to identify the characteristics of a bad feminism in literature, with a view to translating the stories. In addition to the formal aspects reminiscent of metafeminism, I analyse the themes addressed in the collection and the construction of female characters through the representation of their agency. Finally, this is followed by an overview of existing approaches and strategies in the field of feminist and gender-focused translation in order to determine those which it would be productive to apply to an ambivalent work such as “Difficult Women”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".