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Record W2606464562 · doi:10.31542/j.muse.393

Fighting the Fear: Everyday Terror in the American Short Story Collection after 9/11 A Study of Jennifer Egan’s A Visit from the Goon Squad and Elizabeth Strout’s Olive Kitteridge

2016· article· en· W2606464562 on OpenAlexaffvenue
Rae-Lee Kruger

Bibliographic record

VenueMacEwan University Student eJournal · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSymbol (formal)HistoryAestheticsArtSociologyMedia studiesPhilosophy

Abstract

fetched live from OpenAlex

Jennifer Egan’s A Visit from the Goon Squad and Elizabeth Strout’s Olive Kitteridge represent the emphatic force that can be created within a short story collection and each contain at their core what has become a fundamental aspect of American literature since September 2001: terror. In A Visit from the Goon Squad and Olive Kitteridge, characters feel and attempt to cope with terror in their everyday lives. Both Egan and Strout contextualize individual terror against the broader national and cultural form felt by the United States after the events of 9/11. The presence of the void left by the Twin Towers is a potent symbol of terror within each collection, paralleling the characters’ experiences with that of post-9/11 America while highlighting the existence of everyday terror and providing a lens for character self-reflection. This essay focusses on two categories of terror that figure into both collections, terror of the unknown and terror of being alone, and how strategies employed by Egan’s and Strout’s characters to cope with this terror correspond to the American public response to the wider terror instilled by 9/11.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.992

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 studies0.0010.000
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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 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

Citations1
Published2016
Admission routes2
Has abstractyes

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