Bibliographic record
Abstract
Abstract The discussion of ethics in the social sciences focuses on ‘doing no harm’ and ‘giving back’ to research participants, but does not explore the challenges of empathy and fear in research with participants in political violence and war. Drawing on 180 in-depth interviews on the Georgian–Abkhaz war of 1992–93 collected over eight months between 2010 and 2013 primarily in Abkhazia, but also Georgia and Russia, I argue that researchers can come to empathize with some but fear other participants in past and present violence. These emotional responses can influence researchers’ ability to probe and interpret interviews and respondents’ ability to surpass strong positions to explore dilemmas of participation in violence. By empathizing with not only ‘victims’ and ‘non-fighters’ as I had expected based on my pre-existing moral-conceptual categories, but also participants in the war, I found that individuals adopted multiple overlapping roles and shifted between these roles in the changing conditions of violence. In contrast, failing to empathize with and fearing those who continued to participate in violence after the war of 1992–93 limited my ability to fully appreciate the complexity of their participation, but shed light on the context of violence in contemporary Abkhazia. This analysis shows that reflection on the role of empathy and fear in shaping our interactions with research participants can help advance our understanding of participation in violence and this difficult research context.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".