Thinking about Cross-Cultural Differences in Qualitative Interviewing: Practices for More Responsive and Trusting Encounters
Bibliographic record
Abstract
Existing methodological efforts subsume the interview into broad epistemological abstractions, neglecting actual mechanics of the interview as practice, and dismiss linguistic and cultural asymmetry in the interview as a matter of (in)adequate resources. Reflecting on 24 semi-structured interviews exploring social media use among Hong Kong youth, this article develops a culturally sensitive approach that democratically exposes the way cultural norms surface in communication, using strategies which (a) transform the dialogical mechanics of an interview—reflecting back and encouraging; (b) transform the positionality of the researcher—building intersubjectivity and emotional rapport; (c) transform the context of the interview—making shifts in space, language, and presentation. In doing so, a culturally sensitive approach generates practical recommendations for (a) humanizing the researcher to dismantle power imbalances and social distances and (b) naturalizing the interview into a more conversational form, both of which combine to expose the cultural logics that govern action and interpretation whilst constructing results into intimate narratives of people’s life-worlds.
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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 | 0.571 | 0.472 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.034 | 0.080 |
| Scholarly communication | 0.033 | 0.034 |
| Open science | 0.011 | 0.042 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".