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Record W3099199344 · doi:10.1017/nps.2020.42

When the “People” Leave: On the Limits of Nationalist (Bio)Politics in Postwar Bosnia-Herzegovina

2020· article· en· W3099199344 on OpenAlexaff
Larisa Kurtović

Bibliographic record

VenueNationalities Papers · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBosnianNationalismPoliticsEthnic nationalismPolitical economyPolitical scienceRestructuringEconomic restructuringScholarshipSociologyGender studiesDevelopment economicsLawEconomyEconomics

Abstract

fetched live from OpenAlex

Abstract This article examines the social and political effects produced by the most recent wave of emigration in postwar Bosnia, widely understood to be the result of continued political instability and economic decline that followed the 1992–95 war. Drawing on ethnographic research in a deindustrialized Bosnian town and analysis of popular discourses seeking to make sense of this new wave of departures, I show how the phenomenon of postwar exit impacts those staying behind and inspires new forms of reflection that link past histories of violence to more recent forms of dispossession. The emergence of such forms of historical consciousness reveals that postwar migration is haunted both by the memory of wartime expulsions and ethnic cleansing, as well as by the often-unacknowledged violence of postwar economic restructuring glossed as the postsocialist transition. In asking what happens to nationalist regimes, as well as scholarship on nationalist politics, when the “people” leave, I demonstrate the need to analyze the ongoing out-migration both in terms of Bosnia’s historical specificity and global political-economic dynamics. In so doing, I show how absences created by these departures create new vantage points that bring to light and expose unsettling political configurations left behind by the Bosnian war.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.013
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.300
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2020
Admission routes1
Has abstractyes

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