The Multilingual Corpus of Survey Questionnaires: A tool for refining survey translation
Bibliographic record
Abstract
This article describes the design and compilation of the Multilingual Corpus of Survey Questionnaires (MCSQ), the first publicly available corpus of international survey questionnaires. Version 3.0 (Rosalind Franklin) is compiled from questionnaires from the European Social Survey, the European Values Study, the Survey of Health, Ageing and Retirement in Europe, and the Wage Indicator Survey in the (British) English source language and their translations into eight languages (Catalan, Czech, French, German, Norwegian, Portuguese, Spanish and Russian). Documents in the corpus were translated with the objective of maximising data comparability across cultures. After contextualising aims and procedures in survey translation, this article presents examples of two types of problematic translation outcomes in survey questionnaires: The first type relates to the choice of idiomatic terms or fixed expressions in the source text. The second type relates to cases where the semantic variation of translation choices exceeds the scope allowed to maintain the psychometric properties across languages. With these examples, we aim to demonstrate how corpus linguistics can be used to analyse past translation outcomes and to improve the methodology for translating questionnaires.
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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 | 0.084 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".