From Data Problems to Data Points: Challenges and Opportunities of Research in Postgenocide Rwanda
Bibliographic record
Abstract
Abstract: While interest in conducting fieldwork in conflict and postconflict societies continues to grow, literature addressing the specific challenges and dilemmas of this kind of research remains scarce. Based on four months of fieldwork and approximately seventy interviews, this article explores the complexities of conducting research in postgenocide Rwanda. I argue that what at first may appear to be data problems can also be important data points; problems such as historical memory, selective telling, and skewed participant demographics illuminate political structures, group relations, and societal cleavages. This article then illustrates this argument by examining how these challenges/opportunities help explain the difficulties involved in teaching history in postgenocide schools. These reflections on research in Rwanda suggest valuable lessons for fieldwork and data analysis in a number of settings by providing examples of pitfalls, dilemmas, and often unseen opportunities that are likely to present themselves in other divided societies.
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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 | 0.343 | 0.431 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.006 | 0.012 |
| 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; 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".