Paleografía Latina: recursos para docentes y estudiantes o sobre cómo no perderse en la Red
Bibliographic record
Abstract
En este artículo se recomiendan páginas web de utilidad para la docencia de las asignaturas comprendidas dentro del área de las Ciencias y Técnicas Historiográficas, en especial para Paleografía. La intención es que cada profesor conozca los recursos disponibles online en relación con esta materia para que pueda servirse de ellos a la hora de complementar su Plan Docente acorde con las nuevas exigencias de la «Era Digital». Del mismo modo, se pretende también proporcionar al alumno recursos útiles para optimizar su metodología de estudio de Paleografía.In this article, I recommend useful websites for teaching the subjects covered within the area of Manuscript Studies, especially for Palaeography. My main purpose is to ensure that every teacher is familiar with the online resources available on this subject and is able to use them to complement her or his Teaching Plan with the new requirements of the «Digital Age.» Similarly, I also aim to provide students with useful resources to optimize their methodology for studying Palaeography.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".