Literary Cross Dressing: Terenci Moix's "Nuestro virgen de los mártires" and "El amargo don de la belleza"
Bibliographic record
Abstract
En Nuestro virgen de los m?rtires y El amargo don de la belleza Terenci Moix se basa en el hibridismo de la novela hist?rica para crear textos travestidos, reflejo de la ambig?edad sexual de los protagonistas de los mismos. Mediante ciertas estrategias textuales se pone en cuesti?n la clasificaci?n de las novelas, estrategias que hacen evidente la falta de un fundamento inmanente de los signos cuya lectura/escritura permite fijar dicha taxonom?a. Seg?n la estrategia del autor, leer/escribir equ?vocamente el g?nero de las obras se equipara a la lectura de la identidad del cuerpo humano bas?ndose en sus signos exteriores. Pero el inter?s de Moix va m?s all? de la sexualidad de sus personajes para sugerir que la presencia del travestismo (textual o corporal) se?ala una crisis epistemol?gica, un conflicto que en las dos novelas apunta a dos epistemes: la historiograf?a y el monote?smo cristiano. Si a la historia le sirve la ficci?n para atribuirse una posici?n preferente en el mundo epistemol?gico, el monote?smo cristiano despliega parecido mecanismo al postular el cuerpo pagano sobre el cual efect?a una reescritura/lectura. Estas instituciones se apoyan mutuamente en su proyecto de fundar su legitimidad, a la vez que revelan una semejanza inicial consistente en la producci?n y diferenciaci?n con un cuerpo abyecto para autolegitimarse.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".