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Record W3215832900 · doi:10.3138/seminar.57.4.2

The Sound of Life in Marcel Beyer’s <i>Flughunde (The Karnau Tapes)</i>

2021· article· en· W3215832900 on OpenAlexvenueno aff
Arina Rotaru

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature and Cultural Memory
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)ColonialismAgency (philosophy)HistoryCommunismSocialismAestheticsCold warArt historySociologyArtPolitical scienceLawPoliticsArchaeologyAcousticsSocial science

Abstract

fetched live from OpenAlex

In Marcel Beyer’s celebrated Flughunde (1995), the discovery of an underground archive of sound in the aftermath of the Cold War—preserved despite strategies apparently calling for its mechanical destruction—reassigns agency and voice to instrumentalized victims of National Socialism. By highlighting the close connection between an alleged security custodian of the archive, the actual National Socialist sound cartographer Hermann Karnau, and Moreau, a character bearing a strong resemblance to the protagonist of H. G. Wells’s 1896 novel The Island of Doctor Moreau, Beyer’s novel draws attention to a utopian experiment with life that was carried out in the wake of the colonial enterprise in the Pacific and posits additional historical undertones manifested in Karnau’s National Socialist experiments with sound. Karnau’s attempt to master vocal timbre in particular foregrounds technologies that make it possible to manipulate voice and memory in the post-Fascist and post-Communist present. In spite of technological alteration, archived voices of colonial and National Socialist subjects manifest a vitalist aesthetic. With its concern for race, sound, and memory, the novel breaks new ground in telling the story of the National Socialist and colonial past in the aftermath of the Cold 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.274
Teacher spread0.231 · 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 designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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