Measuring Trainee Translators’ Knowledge of German Culture. Design and Validation of a Cultural Declarative Knowledge Questionnaire
Bibliographic record
Abstract
In the context of a quasi-experimental study on acquisition of cultural competence by trainee translators in the case of German-Spanish translation, a declarative knowledge questionnaire on German culture was designed and validated. This instrument, which consists of 30 items covering four cultural fields, was designed to gather data on the subjects’ declarative knowledge of German culture before performing a translation task. This paper presents the design and validation of the questionnaire, which was based on verification of content validity, criterion validity and face validity. Teachers of German as a second foreign language in the Bachelor’s degree in Translation and Interpreting at the Universitat Autònoma de Barcelona, students of German as a first and second foreign language in the same degree programme, and citizens of the Federal Republic of Germany participated in the validation of the instrument. This resulted in a validated questionnaire that may be useful for other researchers and teachers. Despite its utility, the instrument has some limitations: it does not enable replication of the study with other samples without calibrating it, it is not stable over long periods of time and it only measures cultural knowledge at a nation-state level.
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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 | 0.032 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".