A Problem-Solving and Student-Centred Approach to the Translation of Cultural References
Bibliographic record
Abstract
Exploring possible answers to questions such as “Can we translate a cultural reference?” or even “What is a cultural reference?” is a highly relevant issue for translation students. 1 These are matters that have been addressed by academics and full time translators alike, and no final or definite solutions have been found to the problems generated by the uncertainties, just as there are no final or definitive definitions of the concept of culture itself. In an attempt to help and guide our students to improve this specific aspect of translation competence, a syllabus was designed within a pedagogical setting based on humanistic and socioconstructivist principles as well as on task and project-based learning, and an experimental study was carried out within that pedagogical setting to explore specific effects of such training. In this article, we will deal mainly with the experimental training itself, whereas the study will be reported on in a forthcoming publication.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".