“Only Applies to Research Conducted in Sweden…”: Dilemmas in Gaining Ethics Approval in Transnational Qualitative Research
Bibliographic record
Abstract
Transnational research funders such as the European Commission and NordForsk increasingly require researchers to conduct transnational research. Yet, there is little research on what this means for seeking ethics approval, not least for qualitative researchers. Much work on ethics approval comes from Canada, the United States, and other Anglophone countries, often in a health-related context, and centers on issues between researchers and research ethics boards (REBs), or on inconsistent or inappropriate decision-making by REBs. Ethical conduct within research has, of course, generated a rich literature but not on gaining ethics approval when conducting qualitative transnational research. Rather, the underlying situation usually is that the research is conducted in the same geopolitical space as where the REB is located. Drawing on two cases studies, in which researchers located in one country, Sweden, sought ethics approval to conduct research in other European countries, we explore some of the challenges that we faced in gaining such approval and provide some suggestions how this process might be made both more efficient and more productive for researchers and research funders alike.
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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 | 0.782 | 0.769 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.039 | 0.127 |
| Scholarly communication | 0.038 | 0.041 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".