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Record W2592384274

Mountain militarism and urban modernity: Balkanism, identity and the discourse of urban-rural cleavages during the Bosnian War

2017· dissertation· en· W2592384274 on OpenAlexfundno aff
Ryan J. Graves

Bibliographic record

VenueSummit (Simon Fraser University) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsMilitarismModernityBosnianGender studiesIdentity (music)Political scienceHistorySociologyAestheticsArtLinguisticsPhilosophyLaw
DOInot available

Abstract

fetched live from OpenAlex

Recent years have witnessed a growth in research addressing the ways in which policymakers, academics and the media characterized the Bosnian war of the 1990s using a variety of problematic discursive frames.However, there has been relatively little scholarship exploring how the conflict was often portrayed as a battle between innocent urban centres and an antagonistic countryside.This thesis uses a discourse analysis of Western and Bosnian textual material to argue that perceptions of the Bosnian war have been characterized by a discourse that attributes the violence to cleavages between urban Bosnians and their rural counterparts.Moreover, this thesis engages with postcolonial theory to demonstrate that this discourse of urban-rural cleavages, in which Western and Bosnian urban self-identity was constructed in opposition to the supposed atavism of the Bosnian countryside, is an advancement of Bakic-Hayden's concept of "nesting Orientalisms."The results of this thesis problematize a common representation of the conflict, expand the concept of nesting Orientalism and help us to understand why urban participation in the ideologies and violence of the Bosnian conflict has often gone unexamined.

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)0.001
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 score0.016
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0160.028
Scholarly communication0.0080.004
Open science0.0010.003
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 head0.012
GPT teacher head0.258
Teacher spread0.247 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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Same venueSummit (Simon Fraser University)Same topicBalkans: History, Politics, SocietyFrench-language works237,207