‘How would you call this in English?’: Being reflective about translations in international, cross-cultural qualitative research
Bibliographic record
Abstract
INTRODUCTION: Medical education researchers increasingly collaborate in international teams, collecting data in different languages and from different parts of the world, and then disseminating them in English-language journals. Although this requires an ever-present need to translate, it often occurs uncritically. With this paper we aim to enhance researchers' awareness and reflexivity regarding translations in qualitative research. METHODS: In an international study, we carried out interviews in both Dutch and English. To enable joint data analysis, we translated Dutch data into English, making choices regarding when and how to translate. In an iterative process, we contextualized our experiences, building on the social sciences and general health literature about cross-language/cross-cultural research. RESULTS: We identified three specific translation challenges: attending to grammar or syntax differences, grappling with metaphor, and capturing semantic or sociolinguistic nuances. Literature findings informed our decisions regarding the validity of translations, translating in different stages of the research process, coding in different languages, and providing 'ugly' translations in published research reports. DISCUSSION: The lessons learnt were threefold. First, most researchers, including ourselves, do not consciously attend to translations taking place in international qualitative research. Second, translation challenges arise not only from differences in language, but also from cultural or societal differences. Third, by being reflective about translations, we found meaningful differences, even between settings with many cultural and societal similarities. This conscious process of negotiating translations was enriching. We recommend researchers to be more conscious and transparent about their translation strategies, to enhance the trustworthiness and quality of their work.
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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 | 0.320 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.023 | 0.060 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".