Is sex lost in translation? Linguistic and conceptual issues in the translation of sexual and reproductive health surveys
Bibliographic record
Abstract
Translated questionnaires are increasingly used in population health research. Nevertheless, translation is often not conducted with the same rigour as the process of survey development in the original language. This has serious limitations and may introduce bias in question relevance and meaning. This article describes and reflects on the process of translating a large and complex sexual and reproductive health survey from English into Simplified Chinese. We interrogated assumptions embedded in taken-for-granted translation practice to locate the sociocultural origins of these assumptions. We discuss how terminology and expression related to sexual and reproductive health may lose their conceptual or linguistic significance during translation in three different ways. Firstly, meanings can be lost in the negotiation of meanings associated with linguacultural and geographical variations of terminology. Secondly, meanings can be lost in the clash between everyday and professional sexual and reproductive health discourses. Thirdly, meanings can be lost due to the design of the source questionnaire and the intended mode of survey administration. We discuss ways to help overcome the unavoidable translation challenges that arise in the process of translating English sexual and reproductive health surveys for migrants from non-English speaking backgrounds.
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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 | 0.442 | 0.567 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".