On Loss and Gain: The Translation of Linguistic Simultaneity in This is How You Lose Her, by Junot Díaz
Bibliographic record
Abstract
Pulitzer-prize winning author Junot Díaz stages culture clashes in his work by dramatizing the linguistic tension between English and Spanish. This strategy, which he calls “linguistic simultaneity” (code-switching), is central in his fiction because it expresses his Latino identity, and it is artistically and politically significant. Translators who wish to recreate his texts for another readership are forced to rethink what translation is and thus to consider new paradigms, since code-switching defies the traditional conception of translation as the transposition from one closed linguistic system to another. Translations into one of the languages that make up the fictional universe of the source text (in this case, Spanish) are especially challenging. Focusing on the extent to which translators transpose the linguistic simultaneity of Díaz’s source texts, this paper explores the possible reader responses to strategies used to maintain or downplay linguistic tension in the target texts. A comparison of two translations into Spanish of Diaz’s short stories “The Sun, The Moon, The Stars” and “Otravida, Otravez” fromThis is How You Lose Her(2012) will illustrate how linguistic simultaneity is recreated.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".