Bibliographic record
Abstract
Les voy a hablar sobre la historia de la traducción, el tema que más me agrada. Titulé mi exposición: “Historiografía, nociones, sentido, citas, cd-rom”, palabras que corresponden a cada una de las cinco partes de mi charla. En la primera parte, sobre historiografía, veremos brevemente cómo, en mi opinión, se debe escribir la historia de la traducción. La segunda parte está dedicada a un proyecto de estudio sobre las nociones de historia de la traducción, y la tercera a la manera cómo los traductores han concebido el sentido a través del tiempo. En la cuarta parte, les hablaré sobre el diccionario de citas sobre la traducción, cuya redacción acabo de terminar, y concluiré con una breve descripción de un instrumento pedagógico de nuevas tecnologías en enseñanza de la traducción: un cd-rom sobre la historia de la traducción.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".