Bibliographic record
Abstract
Con frecuencia la falta ocupa el centro de nuestra percepción del aprendizaje de la traducción, como lo muestran los glosarios de los manuales de traducción. Esta tendencia se manifiesta en la manera en que se evalúan los trabajos de los estudiantes, ya que el profesorado a menudo se limita a corregir las faltas. Es por ello que, según afirman ciertos especialistas en didáctica, el temor a cometer una falta puede causar ansiedad en el estudiante y contribuir a crear un ambiente poco propicio para el aprendizaje. Por lo tanto, es conveniente diferenciar la falta respecto del error: la primera puede ser considerada como inhibitoria y como sinónimo de fracaso, mientras que el segundo puede servir como fundamento para una “reconstrucción” del conocimiento. El error resulta, de hecho, una valiosa herramienta pedagógica que, sin embargo, conviene manejar con precaución. En traducción, el profesor sólo estará en condición de ayudar al estudiante a progresar si conoce el tipo de faltas que éste es susceptible de cometer. En este sentido, resulta pertinente sancionar las faltas al principio y después guiar al alumno de manera que comprenda el origen de sus errores, con el objetivo de evitar la recurrencia. AbstractThe notion of mistake has often been in the centre our perception of translation training, as can be seen in the glossaries in translation manuals. This tendency is evident in the way teachers assess students’ work: their role often consists entirely in correcting mistakes. And yet, as some specialists in didactics point out, learners may be anxious and stressed by the fear of committing mistakes, a situation which is not propitious to learning. But beyond the mere notion of correcting mistakes, which may be inhibiting and considered as emphasizing failure, mistakes or errors may be used as a substructure leading to ‘rebuilding’ of knowledge. An error may be a valuable educational tool, but it must be used with the greatest caution. In translation training, a teacher will often be able to help students make progress only if he/she is aware of the type of errors students are prone to making: it thus may be very useful to correct errors then to offer guidance to learners so that they understand the source of their errors in order to avoid their recurrence. Recibido: 04 de marzo de 2013Aceptado: 11 de abril de 2013
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".