Bibliographic record
Abstract
La deformación literaria de las mujeres épicas. Este libro es un estudio detallado de los papeles jugados o atribuidos a los personajes femeninos en la épica medieval española. Examina cómo las mujeres en la literatura histórica española han sido transformadas en el teatro, lapoesía y la novela desde la Edad Media hasta autores del siglo veinte como Juan Goytisolo, Antonio Gala, María Teresa León y Lourdes Ortiz. Aprovechando el concepto de Walter Benjamin del grano de trigo preservado a través de los tiempos, explica cómo la memoria histórica de los hechos de estas mujeres ha sido deformada y olvidada. Las necesidades estéticas y, sobre todo, las ideologías dominantes afectaron sus representaciones literarias. Desde tiempos medievales hasta el presente, los autores que estudiamos glorifican a los personajes masculinos sin manchar la fama del héroe o criticar al monarca. La enorme mayoría de los textos restan valor a la contribución de las figurasfemeninas como agentes de cambio, o las usan como chivos expiatorios de crímenes masculinos. El retrato cambiante y predominantemente negativo de las mujeres épicas permite que la memoria colectiva nacional de los mitos fundacionales españoles pueda sobrevivir y ser narrada de diversas formas. MARJORIE RATCLIFFE es profesora en la Universidad de Western Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".