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Record W2809943687 · doi:10.1080/08865655.2018.1462238

Securitizing a European Borderland: The Bordering Effects of Memory Politics in Bosnia and Herzegovina

2018· article· en· W2809943687 on OpenAlexvenueno aff
Sarah Sajn

Bibliographic record

VenueJournal of Borderlands Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsnot available
Fundersnot available
KeywordsBosnianPolityPoliticsNationalismPolitical scienceEuropean unionSecuritizationPolitics of memoryPolitical economyState (computer science)SecessionBosnia herzegovinaEthnic CleansingLawSociologyEthnology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.315
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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