Collective Memories and Legacies of Political Violence in the Balkans
Bibliographic record
Abstract
Abstract This special issue builds on empirical research to provide new insights into the interrelations between collective memory and legacies of political violence in the Balkans. The contributions pay particular attention to two major issues: First, they explore the ways in which individuals and groups respond to and cope with violent pasts by investigating commemorative practices including public performances, narratives, and negotiations of counter-memories. Second, they make explicit how people select and reassemble collective memories through remembering violent pasts to create and disseminate novel forms of identity. Through interdisciplinary lenses, the studies reveal how the legacies of political violence and their lived experience become important means for people to create and mobilize collective memories that are influential enough to shape nationalistic and political realities on the ground. On a theoretical level, the articles demonstrate various ways in which collective memories enable critical discussions around a wider set of issues including national identity, nationalism, making of history, and local power games. By engaging with these concepts, the contributions question dominant framings of past events as they investigate how counter-memories and counter-powers emerge in the process of negotiating established versions of history, official narratives, and hierarchies of power.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".