“The Suitable Language of Love”: Confessional Discourse in By Grand Central Station I Sat Down and Wept
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.033 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".