Enseñar la teorÃa de la traducción : diseño de competencias y explotación de recursos pedagógicos
Bibliographic record
Abstract
The progress that has been made in the field of pedagogy, especially over the last 30 years, has brought about a profound change in the conception of the teaching/learning process. The constructivist approach has been largely responsible for the development of the communicative and task-based approaches that currently predominate in the teaching of languages, as well as in the teaching of translation. Both the adoption of the principles set out in the Bologna Declaration by the Spanish university system and the European Harmonisation projects that are being carried out at the Universitat Jaume I (which this work is part of) make it quite obvious that this change is essential to be able to integrate, activate and enhance the student’s role in the teaching/learning relation. Within the context of the Degree in Translation and Interpreting this turns out to be relatively easy and extremely fruitful in practical subjects, but becomes far more difficult in more theoretical subjects. In this latter case, the master class is the most commonly used educational resource and there are often over a hundred students in the lecture room. An example of such a situation is the subject «Translation theory and methodology», which students study in the first year of their degree course. In this article we will outline some of the changes in methodology that have been introduced in this subject in recent years. We will set out from the concepts of vertically integrated teaching and translation competence and then go on to focus on the design of objectives, learning outcomes and educational resources.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".