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Record W3150994369 · doi:10.1515/9780822394297

Vampire nation : violence as cultural imaginary

2011· book· en· W3150994369 on OpenAlexaboutno aff
Toma Longinović

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2011
Typebook
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsVampireThe ImaginaryLiteratureBloodyNationalismMasculinityPoliticsArtMetaphorHistoryGender studiesSociologyPsychologyPolitical sciencePsychoanalysisPhilosophyLinguisticsLaw

Abstract

fetched live from OpenAlex

Vampire Nation is a nuanced analysis of the cultural and political rhetoric framing 'the serbs' as metaphorical vampires in the last decades of the twentieth century, as well as the cultural imaginaries and rhetorical mechanisms that inform nationalist discourses more broadly. Tomislav Z. Longinović points to the Gothic associations of violence, blood, and soil in the writings of many intellectuals and politicians during the 1990s, especially in portrayals by the U.S.-led Western media of 'the serbs' as a vampire nation, a bloodsucking parasite on the edge of European civilization.Interpreting oral and written narratives and visual culture, Longinović traces the early modern invention of 'the serbs' and the category's twentieth-century transformations. He describes the influence of Bram Stoker's nineteenth-century novel Dracula on perceptions of the Balkan region and reflects on representations of hybrid identities and their violent destruction in the works of the region's most prominent twentieth-century writers. Concluding on a hopeful note, Longinović considers efforts to imagine a new collective identity in non-nationalist terms. These endeavors include the emigrant Yugoslav writer David Albahari's Canadian Trilogy and Cyber-Yugoslavia, a mock nation-state with "citizens" in more than thirty countries

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.255
Teacher spread0.232 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

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