The danger of a single story: Iconic stories in the aftermath of the 1994 Rwandan genocide
Bibliographic record
Abstract
In the aftermath of the 1994 genocide, the government of Rwanda—much like other transitional regimes around the world—has prioritized reconciliation initiatives that educate civilians with a highly politicized understanding of the conflict and encourage them to speak about the conflict and its aftermath in a manner that reinforces the legitimacy of the current government. However, individual survivors, bystanders, ex-combatants, and/or perpetrators of the genocide find various subtle ways to reinforce, resist, or complicate the current official history. This article analyzes a series of “iconic stories” that are repeated by Rwandans in different settings due to their historical and personal resonance for what they can tell us about the ethnic and political tensions that often continue to divide Rwandans and the overall challenges associated with everyday life since the genocide. Yet, engaging with these iconic stories places the researcher in a difficult position where the democratizing potential of oral history is potentially undermined. This article argues that even while qualitative researchers have an obligation to listen deeply to their informants, their moral and professional obligations to avoid reproducing narratives that promote potentially reprehensible agendas—for example, genocide denial or the legitimation of authoritarianism—make contextualizing their participants’ narratives in relation to the personal, historical, and political climate in which they are being produced essential.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".