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Record W2792141847 · doi:10.3968/9984

Topoanalysis as Narrative Technique in John Cheever’s Architecture of Short Fiction

2017· article· en· W2792141847 on OpenAlexvenueno aff
Farzaneh Doosti

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldPsychology
TopicNostalgia and Consumer Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeParanoiaMainstreamConsciousnessLiteratureAestheticsHistoryPsychoanalysisSociologyPhilosophyArtPsychologyEpistemology

Abstract

fetched live from OpenAlex

The backlash of recent biographies of the American “Chekhov of Suburbs” as an ill-tempered alcoholic bisexual with sharp edges of paranoia might serve to justify Cheever’s clumsy, fragmented narratives of grumpy middle class American male commuters who are about to drown in their matrimonial abyss. The present paper’s approach is, however, to avoid psychobiography in favor of stylistic defense. Not quite as psychologically neurotic a writer as what the mainstream biographers have claimed, Cheever mastered the architectural design of fiction. An examination of a number of these short stories (excluding his longer novels in which fragmentation is an undeniable weakness) lays bare a kind of spatial consciousness: the Bachelardian notion of topoanalysis as the dominant technique. Whereas the public and private boundaries are naturally trespassed in many stories, such as Another Story and The Enormous Radio, in some others the protagonists embark on an intentional interference in space – from erasing a whole town in Geometry of Love to living another man’s life in Seaside Houses. Topoanalysis seems to be Cheever’s favorite narrative technique to reach phenomenological borders of life, oftentimes in unhomely circumstances where one’s totality is menaced by internal and external forces.

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 score0.677
Threshold uncertainty score0.419

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 head0.021
GPT teacher head0.391
Teacher spread0.370 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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