Análisis de los rasgos lingüísticos de Maus y sus interferencias en la traducción al español
Bibliographic record
Abstract
Art Spiegelman abrió con Maus (1980-1991) un nuevo camino para la novela gráfica a nivel internacional: entrevistando a su padre, que le cuenta sus memorias sobre el Holocausto, presenta una historia de carácter confesional, inédita hasta entonces en este ámbito de manifestación artístico-literaria. Junto con la impactante representación de los personajes, destaca especialmente la historia de supervivencia en primera persona. En este trabajo, analizamos la importancia del lenguaje en Maus, y más concretamente los rasgos lingüísticos que caracterizan la forma de expresión del protagonista, cuya lengua materna no era el inglés, sino el polaco. Son numerosas las incoherencias y errores intencionados en el original (por ejemplo, “… I can tell you other stories, but such private things, I don’t want you should mention”). Para ello, trataremos de determinar si existen en estas incoherencias parámetros recurrentes y posibles influencias de otra(s) lengua(s). Finalmente, analizaremos cómo han podido interferir estas pautas en la traducción al español de una de las novelas gráficas más destacadas del siglo XX y la primera ganadora del Premio Pulitzer en 1992.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".