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Record W1949804687 · doi:10.3138/cras.2014.013

White Rain: 9/11 and American Fiction

2015· article· en· W1949804687 on OpenAlexvenueno aff
Ira Nadel

Bibliographic record

VenueCanadian Review of American Studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeSymbol (formal)White (mutation)Event (particle physics)HistoryAestheticsLiteratureVariety (cybernetics)Tragedy (event)Construct (python library)PhraseFace (sociological concept)FLAGS registerArtPhilosophyLinguisticsComputer science

Abstract

fetched live from OpenAlex

Abstract: How have authors responded to 9/11? This article examines a variety of reactions in light of the need to construct a dominant narrative necessary to restore a sense of security and understanding. But no single work or group of works has so far managed to trump the tragedy, and a void remains. The phrase “white rain” expresses the blizzard of paper generated by authors attempting to make sense of the event. Authors as diverse as John Updike, Don DeLillo, Frédéric Beigbeder, Jonathan Safran Foer, and Thomas Pynchon are the focus of the analysis here, showing how newly opened textual spaces are filled with works alternating between history and fiction. Of particular importance is the role of film, the importance of fear, and the symbol of flags in evaluating the process of recovery. Of particular note is the unease that characterizes the reaction of writers to the challenge they face: how to unite 9/11 as it exists in our cultural imagination with the event itself. That is the new task of literature in the post-9/11 age which, ironically, seems to require conflict or catastrophe for its inspiration.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.282
Teacher spread0.230 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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