Developing Trainee Translators’ Strategic Subcompetence Through Metacognitive Questionnaires
Bibliographic record
Abstract
This paper presents a case study carried out in a two-part German-Spanish general translation course. It results from a pedagogical intervention aimed at helping first-year translation students to develop their strategic subcompetence through metacognitive questionnaires. It focuses on a single function of this subcompetence, the evaluation of trainees’ translating, which was carried out by using post-translation metacognitive questionnaires. These were meant for trainees to reflect on certain aspects of their translating. The most relevant ones were the identification of translation problems and the justification of their solutions. Both aspects were addressed by a twofold question aimed at helping students to identify adequately-solved problems and to justify their solutions. An analysis of students’ answers to this question reveals that the most frequently identified items were strategically relevant problems to do with general conventions of style and genre-specific expressions. Solutions to these problems were also the most frequently justified, and references to successfully applied translation strategies increased from one part of the course to the other as a trace of gradually developing strategic subcompetence.
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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 | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".