Bibliographic record
Abstract
Newly available documentation from the State Archive of Bosnia-Herzegovina indicates that the majority of sites where Muslim civilians were killed during the Second World War remained unmarked as late as the mid-1980s. The existing scholarship, most of which argues that Yugoslavia’s communist regime sought to “de-ethnicize” the remembrance of all of the interethnic violence of the war, has failed to notice and explain this apparent bias against Muslim civilian war victims. This article seeks to answer the question of why so many sites in Bosnia-Herzegovina where Muslim civilians were killed remained unmarked after the war. It does so through the reconstruction and analysis of the wartime and postwar history of Kulen Vakuf, a small town located in northwestern Bosnia. The analysis of the dynamics of mass killing in the region reveals that the communist-led Partisan movement absorbed large numbers of Serbian insurgents who had murdered Muslims earlier in the war. The transformation of the perpetrators of the massacres into Partisans created a postwar context in which the authorities, to avoid implicating insurgents-turned-Partisans as war criminals, and the Muslim survivors, out of fear of retribution and a desire to move on, agreed to stay silent about the killings. The end result was the absence of monuments for the victims.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".