Bibliographic record
Abstract
En su novela Sefarad, Antonio Muñoz Molina logra sumergir el lector en un mundo repleto de olores y sabores, objetos, lugares y circunstancias, y sensaciones físicas y emocionales que desencadenan una reacción física en el lector que sobrepasa la lectura pasiva del texto. A base de las ideas de Heidegger, Gumbrecht, y otros, se desarrolla aquí una manera sobretextual de aproximarse el lector al texto. Aplicando las ideas de Stimmung, veremos cómo el autor ha logrado injertar en los recuerdos de su narrador sensaciones que individualizan la experiencia vivida. Lo significativo de esa técnica es la capacidad de esas descripciones de afectar al lector, ligando sus experiencias y recuerdos con los del narrador. Esa técnica complementa y aumenta la importancia de la identidad individual frente a su neutralización en el contexto de una identidad nacionalista, sutilmente representado en el contexto franquista. Como sugiere el título, Sefarad recalca el papel que juegan los elementos culturales (comida, aromas, lugares, celebraciones, etc.) que estimulan al individuo y causan una reacción física (e.g., escalofríos, hambre, salivar, lágrimas). Como resultado, la novela sirve como propaganda antinacionalista y reafirma la peculiaridad de la identidad individual y cultural que define a un pueblo.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".