Neutral evaluators or testimonial connoisseurs? Valuing and evaluating reconciliation in post‐genocide Rwanda
Bibliographic record
Abstract
Countless reconciliation initiatives – state and non‐state, local and international – have emerged to redress the legacies of the 1994 genocide in Rwanda. Based on fieldwork with two Rwandan peace‐building organisations, this article takes an ethnographic perspective on how these organisations measure or evaluate ‘how reconciled’ Rwandans are. Organisations’ measurements of reconciliation are based on testimonies they collect from genocide survivors and perpetrators. They read ‘indicators’ into these testimonies to quantify the progress of reconciliation in a given region, but their process of deriving those numbers from testimony is never clear. I argue that organisation staff do not only stake their expertise on ‘objective’ measures of reconciliation that manage the ambiguities of testimony, but also on their performance of gifted subjective intuition to discern ‘authentic’ testimony from that which conceals ongoing enmity. As such, anthropological understandings of modern evaluative practices must take seriously both subjectivity and objectivity as potential sources of power and authority. In the end, evaluating reconciliation may not only be driven by organisational or political demands to produce metrics, but also by organisation staff's search for confirmation of their own worth in the post‐conflict recovery project and for signs that violence will not erupt in Rwanda again.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".