Translation and the Authorial Image: the Case of Federico García Lorca’s Romancero gitano
Bibliographic record
Abstract
Despite Barthes’s claim that the author is dead, leaving the scene for his work, freed from its all too personal origin, I would like to argue that the author image is far from absent in the practice of literary translation. On the one hand, the author’s image within a particular literary and social system may determine which work is translated, and even how it is translated. On the other hand, it seems likely that some characteristics of a persona will be highlighted more than others, depending on which source texts are selected for translation and on how the author and his or her works are presented in prefaces and commentaries accompanying the translations. Moreover, the translation strategy may enhance the prevailing tendencies within reception and thus contribute to a certain perception of the author in the target culture. In this paper I will investigate these hypothetical connections, taking as an example the Spanish author Federico García Lorca and a number of translations of his Romancero gitano (1928) into French, English, and Dutch. I will examine a possible correlation between the prevailing “folkloristic” image of Lorca in the early literary criticism, and the emphasis on romantic, naïve and mythological aspects in translations of his work, and conversely, the later, more complex and gloomy image presented of the author, and translation strategies which highlight elements that correspond to that view.
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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 | 0.013 | 0.021 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".