Reescrituras digitales de la emigración española. El poema que cruzó el Atlántico de María Mencía
Bibliographic record
Abstract
La dinámica inherente a la escritura digital permite abordar la creación literaria como una experiencia en la que los temas y las formas adquieren una distinta proyección sobre el lector. Este artículo analiza el proyecto de María Mencía, El poema que cruzó el Atlántico (2017), al objeto de revisar cómo se aborda, desde la poética digital, la reescritura del episodio de la travesía del Winnipeg, ocurrido en agosto de 1939 con dos mil refugiados republicanos a bordo. A partir de la profundización en una única metáfora, construida alrededor de dicho viaje, la autora rinde homenaje a la memoria como elemento regenerador de la lectura y la escritura desde un punto de vista retórico y estético. The inherent dynamics of digital writing allows an approach to literary creation as an experience in which themes and forms acquire a distinct projection on the reader. This article analyzes María Mencía's project El poema que cruzó el Atlántico (2017) in order to address how digital poetics rewrites the Winnipeg voyage (1939), an episode of the Spanish Civil War which occurred in August 1939 with two thousands Republican refugees on board. By exploring a single metaphor, built around the voyage of the Winnipeg, the author pays homage to memory as a regenerating element of reading and writing from a rhetorical and aesthetic point of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".