Humanidades digitales: la censura y los laudatorios en las preliminares del Siglo de Oro español; Madrid y Guzmán de Alfarache
Bibliographic record
Abstract
Este artículo presenta los resultados del estudio de la censura y los laudatorios en algunas preliminares de la literatura del Siglo de Oro español. Fue realizado con la metodología de “queries” en la plataforma Sylvadb del Laboratorio Culturplex. El trabajo se divide en cuatro partes: introducción, donde se definen los elementos teóricos desde los cuales se aborda la investigación; metodología, en la que se tratan los pasos de la investigación desde la lectura, la elaboración de la base de datos, las consultas (searchs) y preguntas (queries); resultados, en los cuales se indican los resultados obtenidos de la investigación, y por último están las conclusiones, que señalan dos asuntos que aportan nuevas propuestas al campo de los estudios literarios respecto a las humanidades digitales y al trabajo sobre las preliminares.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".