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Record W4230040384 · doi:10.1353/crv.2007.0013

TheTriumph of Death: National Security and Imperial Erasures in Don DeLillo's Underworld

2007· article· en· W4230040384 on OpenAlexvenueno aff
David Noon

Bibliographic record

VenueCanadian Review of American Studies · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesCold warEthnologyComplicityArtComicsHistoryArt historyLiteraturePolitical scienceLaw

Abstract

fetched live from OpenAlex

Writing against the dominant tendencies of popular historical memory in the 1990s, Don DeLillo's Underworld depicts the civic and ecological catastrophes left in the wake of the cold war. In particular, DeLillo surveys the wreckage of the American public sphere in ways that are both comic and distressing. Yet Underworld's critique remains partial. Although Underworld highlights the complicity of the national security state in the erosion of national community, he is nevertheless unable to fully imagine the consequences of the cold war for other communities, especially Native American communities in the nuclear West, who were most severely trampled by it. Allant contre la tendance dominante de la mémoire historique populaire des années 1990, le roman Underworld de Don DeLillo raconte une catastrophe culturelle et écologique issue de la guerre froide. En particulier, DeLillo fait une analyse à la fois comique et désespérante de l'effondrement du domaine public américain. Pourtant, la critique que fait Underworld demeure partiale, car, bien que le roman souligne la complicité de l'é tat et de la sécurité nationale dans l'érosion de la communauté, l'auteur n'est guére en mesure d'imaginer pleinement les conséquences de la guerre froide sur les autres communautés, surtout les communautés autochtones de l'Ouest, lesquelles ont été particuliérement bafouées par l'é tat.

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: none
Teacher disagreement score0.944
Threshold uncertainty score0.976

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 studies0.0000.001
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 head0.039
GPT teacher head0.305
Teacher spread0.265 · 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
Published2007
Admission routes1
Has abstractyes

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