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Record W2510017249

“The Suitable Language of Love”: Confessional Discourse in By Grand Central Station I Sat Down and Wept

2014· article· en· W2510017249 on OpenAlexaffvenue
Myra Bloom

Bibliographic record

VenueStudies in Canadian Literature · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConfessionalConfession (law)Rhetorical questionAmbiguityRhetoricSociologyPleasurePlot (graphics)AestheticsLiteratureLawPhilosophyArtPsychologyPoliticsLinguisticsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Confession occupies a prominent role in Elizabeth Smart’s By Grand Central Station I Sat Down and Wept , where it functions both as an important plot point and the novel’s rhetorical mode. It has also characterized much of the critical discourse surrounding the novel, which many readers and critics have interpreted as Smart’s personal confession rather than as a work of fiction. What is particularly ironic about the autobiographical readings of By Grand Central Station, however, is that the novel formally and thematically resists the demand for disclosure. In the first section of this paper, I discuss Smart’s formal obstructions to this demand, which include self-fictionalization, metatextuality, and paratextual ambiguity. In the second section, I demonstrate how confessional rhetoric within the novel itself likewise thwarts the desire for disclosure by producing pleasure instead of what Foucault calls “knowledge-power.” By disconnecting her statements from empirical reality, referring them instead to a metaphorical structure in which “love has other laws,” the narrator challenges the social and legal condemnation of her extramarital relationship. She simultaneously performs an elaborate “rhetorical seduction” of the reader, persuading her to suspend her moral judgment and embrace the celebration of erotic love.

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: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.975

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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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
Published2014
Admission routes2
Has abstractyes

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