La traduction des questionnaires et des tests: Techniques et problèmes
Bibliographic record
Abstract
OBJECTIVES: To summarize the difficulties involved in translating tests, to describe the translation methods and the test validation procedures, and to apply those to a personality test. METHOD: The revised Freiburg Personality Inventory (FPI-R) was translated, then subjected to the following test validation methods: backtranslation, pretest, and review by a carefully selected expert committee. RESULTS: We used a literature review to clarify FPI-R translation problems. These include in particular the different types of equivalence between the source language and the target language (for example, semantics and idioms, as well as experiential and conceptual equivalence). Statistical validation procedures are employed in principle only. CONCLUSION: The current method combining translation with backtranslation is not sufficient and must be used with, at least, a pretest and step-by-step review by an expert committee. The presence of unilingual experts to explain the smallest details of the target language, which bilingual experts could miss, seems to be mandatory.
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 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 | 0.168 | 0.315 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".