Providing English and native language quotes in qualitative research: A call to action
Bibliographic record
Abstract
BACKGROUND: When publishing qualitative research in international journals, researchers studying non-English-speaking participants provide quotes in English language. This is an issue of increasing concern given the need to be rigorous to represent a diversity of participants within their context, beyond how language (alone) situates them. AIM: To argue for providing English and native language quotes in qualitative research reports. DESIGN: Discussion. METHODS: This paper is based on the literature on use of quotes and translation in qualitative research and authors' experiences of publishing qualitative research. RESULTS: Provision of native and English language quotes may allow for greater transparency of findings, thereby reflecting that the researchers adequately captured the socially and culturally dependent experiences of participants. CONCLUSIONS: Presentation of findings with eloquent quotes serves as the gateway into the sociocultural experiences of individuals. We argued against the norm of providing translated quotes in qualitative reports and build a case for the provision of native as well as English language quotes to promote cross-cultural understanding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.753 | 0.755 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.038 | 0.116 |
| Scholarly communication | 0.043 | 0.082 |
| Open science | 0.016 | 0.058 |
| Research integrity | 0.049 | 0.065 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".