Sharing Oral History With Arctic Indigenous Communities: Ethical Implications of Bringing Back Research Results
Bibliographic record
Abstract
This article discusses ethical implications when sharing results in oral history research. We look at a case study of an Arctic community in Russian Lapland dealing with boarding school experiences. Bringing back research results about this topic provoked diverse reactions. We examine how the social life of stories and the social life of research are interconnected. By questioning the strict applicability of preformulated ethical research principles, we conclude that bringing back research results poses an opportunity to negotiate an appropriate form of reciprocity in research and to gain a deeper understanding of social processes in the communities under study. We identify principles of long-term engagement, collaborative methodologies, and inclusion into the cultural intimacy of the participating community as preconditions for a robust ground for ethics in oral history research.
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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 | 0.205 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.066 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.007 | 0.007 |
| 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; 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".