‘How would you call this in English?’: Being reflective about translations in international, cross-cultural qualitative research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.220 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it