From Data Problems to Data Points: Challenges and Opportunities of Research in Postgenocide Rwanda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it