Securitizing a European Borderland: The Bordering Effects of Memory Politics in Bosnia and Herzegovina
Bibliographic record
Abstract
Historically located at the crossroads of multiple political entities, Bosnia and Herzegovina (BiH) has been constructed as a European borderland. Since the war of the 1990s, its ethnic and religious diversity has been framed as a security threat. European institutions and Member States are politically, economically and military involved in the state-building and reconciliation processes, set as part of BiH’s path towards the Union. In 2014, Sarajevo was placed at the “heart of Europe” in the opening commemoration of the First World War organized by European embassies and their Bosnian partners. The official narrative that pledged for a century of peace after the century of wars suggested the positive impact of European integration on BiH’s violent past. However, local activists claimed divergent interpretations, be it from a nationalist, anti-imperialist or emancipatory perspective. While the commemoration exulted national divisions, it contributed to the construction of BiH as an unstable borderland, which needs to be pacified. Relying on memory and border studies, this article demonstrates that the attempt to institutionalize a pacified memory of the war resulted in legitimizing the European institutions’ domination over Bosnian polity. In fine, it shows how memory politics participates in the securitization of a European borderland.
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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 | 0.001 |
| 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 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".