Interpreter-facilitated cross-language interviews: a research note
Bibliographic record
Abstract
This research note focuses on interpreter-facilitated cross-language qualitative interviews. Although researchers have written about strategies and procedures for working with interpreters, rarely have they offered adequate detail to determine the relative merits of various approaches, and little attention has been paid to the influence that interpreters have on the validity of qualitative data. We advance this body of literature by describing and critically examining the strategies and procedures we used to work with an interpreter to conduct qualitative interviews with Mandarin-speaking grandparents who participated in our study of intergenerational social support during the transition to parenthood. In addition, we examine the influence that our strategies and procedures had on the data generation process and on the validity of the data. Drawing on our experiences, we argue that with adequate preparation, validity checks, and the supplementary strategies that we describe in this article, an interpreter-facilitated interview approach to generating data in cross-language studies can be an effective alternative to more commonly used and more laborious and expensive translation practices.
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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 | 0.113 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".