Topoanalysis as Narrative Technique in John Cheever’s Architecture of Short Fiction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".