Un palimpsesto indeseado en la obra literaria de Juan benet: Julio Cortázar y sus minúsculas pedanterías.
Bibliographic record
Abstract
Se estudia la influencia de Julio Cortázar en Juan Benet, partiendo de las entrevistas hechas a este último. Este lo criticó con virulencia y superficialmente pero, tras un estudio comparativo de sus respectivas obras literarias, se han advertido una serie de concomitancias entre ellos. Ambos relativizaron y descentraron los conceptos de realidad, de lógica y de tiempo y sus instrumentales epistemológicos, narratológicos o lingüísticos. Lo hicieron para elevar el nivel intelectual de los lectores y para así poder penetrar más en lo irracional, espacio de una razón «ultraperceptible» útil para nuestra evolución. I study Julio Cortázar´s influence in Juan Benet, based on the interviews made to the latter. He criticized him with virulence and superficially but, after a comparative study of their respective literary works, I found some similarities between both of them. they minimized and decentered the concepts of reality, logic and time as much as they did it with their epistemological, narratological or linguistic tools. they did that in order to raise the intellectual level of the readers and so as to penetrate more in the irrational, the space of a reason beyond perception and useful for our evolution.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".