When the “People” Leave: On the Limits of Nationalist (Bio)Politics in Postwar Bosnia-Herzegovina
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".