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Record W3014726934 · doi:10.7202/1068205ar

Measuring Trainee Translators’ Knowledge of German Culture. Design and Validation of a Cultural Declarative Knowledge Questionnaire

2020· article· en· W3014726934 on OpenAlexvenueno aff
Christian Olalla-Soler

Bibliographic record

VenueMeta Journal des traducteurs · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsGermanFace validityPsychologyBachelorTarget cultureCompetence (human resources)Context (archaeology)Foreign languageComputer scienceMathematics educationPedagogyLinguisticsPsychometricsSocial psychologyPolitical scienceDevelopmental psychologyGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.867
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.186
GPT teacher head0.318
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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