Mass Graves and the Politics of Reconciliation: Construction of Memorial Sites after the Srebrenica Massacre
Bibliographic record
Abstract
Burial is an integral part of reconciling with death. In this way, mortuary practices are made for the living; and the manner of death and burials continue to affect the politics of the living. Especially after collective traumatic events such as the Srebrenica massacre, reburials become central to the reconciliation process of the surviving communities. The process of reburial, however, also facilitates the claims that a particular territory is part of a specific, ethnic ‘homeland’. As reburials aim to forget the atrocities, they also commemorate them. Although reburial is one of the few ways of moving on after the death of a loved one, it simultaneously claims territory for the communities whose dead are buried there, potentially reigniting tensions in the future. Reburial allows communal reconciliation, but only for the community of the victims. For the community of the perpetrators, such a reburial only serves as a humiliation and inhibits harmony. Attempting to reconcile post-conflict multi-ethnic communities is thus impossible without understanding the profound effects that the community of the dead continues to play in the lives of the living.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".