A Practical Proposal to Use Venuti’s ‘Minoritizing Translation’ for Native American Literature
Bibliographic record
Abstract
In a recent article, I argued that Native American literature, as a minor literature according to Deleuze and Guattari, is a great candidate for being translated in a minoritizing way, as proposed by Venuti. Since this literature is very popular in Spain –13 translations published in the 2010s–, I analysed the most recent translations of Sherman Alexie’s, Louise Erdrich’s and N. Scott Momaday’s novels and concluded that they were aimed at entertainment, at linguistic and syntactic fluency, and at over-refined stylistics. This kind of translation means, hence, the erasure of indigenous cultural and literary aspects from the target texts and the hiding of the socio-political implications of the source texts. In the present article, I insist on the idea that Venuti’s ‘minoritizing translation’ can be adapted to attend to the minor literature features of American Indian books and, consequently, to produce culturally and socio-politically engaged translations. After revising Venuti’s proposal and Tymoczko’s criticism on it, I present a brief description of the translations of works by Alexie, Erdrich, Momaday and Zitkala-Ša, all published during the 2010s. Then, I detail the precise strategies that would help to emphasize the specific characteristics of this literature, and I compare passages from the published translations with my alternative minoritizing translations.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".