Perpetrators as Victims? Inclusivity and Proximity in Post-Genocide Cambodia and Rwanda
Bibliographic record
Abstract
In post-genocide Cambodia and Rwanda, low-level perpetrators often identify as victims of the genocidal regimes alongside those they tortured and killed. However, state and societal responses to these claims appear to have varied dramatically. In Cambodia, the government and civil society organizations seem to view former Khmer Rouge cadres’ claims to victim status as socially acceptable and politically useful, while in Rwanda, the government and civil society organizations have firmly rejected perpetrators’ efforts to claim space as victims. What accounts for these different societal responses in Cambodia and Rwanda? At present, there is ample literature on how authoritarian government actors in both contexts have shaped their nation’s post-genocide transitional justice responses to prevent future bloodshed, while simultaneously reinforcing their regimes’ sometimes tenuous political legitimacy. However, this article offers complementary insights by exploring two otherwise under-researched factors that we argue further inform these polar-opposite reactions to perpetrators’ claims to victim status: (1). post-genocide governments’ offers of inclusivity in defining who is part of each nation’s ideal post-genocide ingroup; and (2). the social proximity of perpetrators and their victims during and after the genocides. Our focus on inclusivity and social proximity related to the Cambodian and Rwandan genocide advances scholarly understandings of the various factors that shape government and social responses to perpetration in the aftermath of genocide internationally.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".