Translation of Cultures and Texts: Envisioning a Culturally Responsive Translational Practice in Qualitative Research
Bibliographic record
Abstract
In this methodological paper, we raise the question of what a culturally responsive translational practice might look like in qualitative research. Through examining the literature on translation in culturally responsive theories and qualitative research methodology, we distinguish two approaches in addressing the issue of translation: translation as texts and translation as cultures. To enact a culturally responsive translational practice, qualitative researchers should maintain an intimately linked dual-focus in their work, attending to both the practical aspects of translation that directly lead to the production of the final translated texts, as well as translation's multi-layered cultural and political effects. This proposal is further unpacked on three levels: (1) On the level of social and cultural processes and structure, we examine the routes and gatekeepers of translation in the context of knowledge production and mobilization; (2) on the level of intersubjective relationality, we explore the significance of visibilizing translation and translators; and (3) on the level of human-text interaction, we consider how interpretive approaches, untranslatability, and styles of translation may shape researchers' translation practice. While drawing insights from culturally responsive theories, we also substantiate our argument using critical translational studies and examples from our empirical research projects. Taken together, this paper outlines some important considerations qualitative researchers should take into account as they envision a culturally responsive translational practice in qualitative research and calls for researchers to engage in this work with multilingual awareness, reflexivity, and criticality.
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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 | 0.426 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.089 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.008 | 0.011 |
| 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".