Reading the Silence: The Picaresque Game of "Lacunae" and Contradiction
Bibliographic record
Abstract
El prop?sito de este estudio es examinar el motivo de los silencios narrativos y contradicciones textuales que caracterizan el discurso picaresco ?ureo. Estas lagu nas son la ant?tesis del lenguaje literario y crean un silencio narrativo que el lector tiene que interpretar activamente. Aunque el autor impl?cito puede tratar de manera diferente a sus lectores en la tradici?n picaresca, las lagunas que deja les inducen a leer activamente para resolver las tensiones, contradicciones e iron?as que est?n presentes. Las causas de estas lagunas son muchas, pero la raz?n m?s significativa para suprimir u omitir materia narrativa es el deseo de convencer (o enga?ar) al lector. Los textos casi siempre nos remiten a motivos tan variados co mo la presencia de digresiones narrativas, la autocensura, la censura inquisitorial o la promesa de una continuaci?n futura. Estas lagunas forman la base de una competencia l?dica entre el autor impl?cito, que intenta esconder aspectos nega tivos de su vida, y el lector, que quiere resolver las tensiones narrativas y descubrir la verdad a pesar de sus implicaciones para el protagonista. Aunque ha resultado casi imposible definir el g?nero picaresco, sugerimos que las lagunas narrativas y el lector activo pueden abrir nuevos horizontes para comprender el fen?meno.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".