“Taking Care” in Intercultural Research
Bibliographic record
Abstract
Methodological and ethical aspects of intercultural research are frequently discussed in the literature. However, rarely are detailed examples given or practical suggestions offered, particularly in relation to qualitative enquiry. Drawing on global health qualitative research by an international team with indigenous women in Guatemala about access to family planning, this article highlights consequences of different research designs and implementation strategies and practices. We used the constant comparison method for analysis and developed a code for portions of interviews or content which might have been omitted had the research been conducted differently. These applied examples are used to illustrate the gaps and misinterpretations possible in intercultural research and how critical it is to involve a local team early and throughout the study in such stages as: preparing research instruments, recruitment, and conducting interviews; multilingual interviewing, transcription, and team analysis; and reporting and dissemination. International research has been likened to an extractive industry due to the propensity of scholars to conduct research then publish only in English, essentially extracting knowledge in a way that is inaccessible to those in the country where research was conducted. Practical and ethical implications are highlighted for those conducting, funding, and reviewing intercultural research, to ensure that research does not become the latest extractive industry.
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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 | 0.263 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.028 | 0.206 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".