From language to translanguaging in qualitative research: Exploring methodological and pedagogical possibilities
Bibliographic record
Abstract
In this paper, we explore the monolingual assumptions enacted in qualitative methodological practice, which often render multilingual linguistic phenomena invisible or frame them as barriers to valid research. We discuss the conceptualization of language in qualitative methodological literature and explore how a theoretical lens of translanguaging might inform us in methodological practices. In particular, we address this question from three aspects: (1) channeling the critique of named languages into the ontological consideration (ways of being and becoming) in qualitative research; (2) making methodological decisions informed by multilingual and, specifically, translanguaging awareness; and (3) breaking down linguistic boundary-keeping and embracing critical and reflexive multilingual practice. By drawing on examples from our empirical research and methodological instruction, we demonstrate that researchers across disciplinary and inter/transdisciplinary fields—including but not limited to migration studies, bilingual education, comparative research, public health, and anthropology—can benefit from such a critical methodological orientation.
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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 | 0.343 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.016 | 0.090 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".