Bibliographic record
Abstract
?Es don Quijote la causa primaria (o agente, en lenguaje escol?stico) del final feliz de la aventura de Sierra Morena, o, por el contrario, es la causa perif?rica (u ocasi?n)? Utilizando la performance theory contempor?nea, y dentro de una tra dici?n cr?tica establecida (Van Doren y Torrente Ballester), el presente art?culo sugiere que don Quijote no est? loco en ning?n momento (ni tan siquiera a veces cuerdo y a veces loco), sino que est? claramente actuando a lo largo de toda la aventura. El examen de tal actuaci?n desde la filos?fica pragmatista (especial mente Nietzsche y Rorty) muestra dos efectos en el texto: la autocreaci?n de don Quijote por la autoexpresi?n y la resoluci?n del problema de los amantes en un proceso ritualista resultado de la acciones, interacciones y relaciones de los perso najes implicados. El final de la aventura nos muestra a un don Quijote no solo agente del desenlace positivo, sino paradigma y pionero de las b?squedas de iden tidad, subjetividad y autenticidad en nuestra propia cultura.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".