Lope de Vega y los lindos: sátira y masculinidad en <i>De cosario a cosario</i>
Bibliographic record
Abstract
En este trabajo examinamos cómo Lope de Vega utilizó uno de los blancos satíricos más importantes de la comedia nueva: el lindo. Para ello, comenzamos presentando una serie de conceptos —como la diferencia entre género y sexo— pertenecientes al campo de los «Masculinity Studies» que resultan de utilidad para analizar los lindos de Lope. A continuación, estudiamos qué era un lindo para los escritores áureos y nos centramos en una de sus características: el tocado, adornado por guedejas, tufos y copetes. Tras notar la presencia de estos peinados en otras obras de Lope (las Rimas de Tomé de Burguillos), las examinamos en una comedia urbana del Fénix, De cosario a cosario, en la que el tema de los lindos es fundamental para la trama. Al hacerlo descubrimos que, más que retratar un tipo existente, Lope contribuyó a crear una figura ridícula que se perpetuó en la literatura de la época y que le servía para fustigar la cultura de la juventud del momento, cuyo gusto poético y teatral también consideraba deleznable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".