Bibliographic record
Abstract
Abstract Critics of Nicole Krauss's Great House (2010) find the novel “downbeat,” “tragic,” and “bleak.” Such interpretations rely on what Peter J. Rabinowitz has called “the rule of conclusive endings” by privileging the narrator of the final chapter. However, as a braided narrative, Great House weaves together five characters who narrate different stories that lead to more hopeful interpretations. The following article proposes that Krauss invites readers to simultaneously privilege multiple, conflicting interpretations by emphasizing the occasions on which her character-narrators speak. Although occasion is the central term in the rhetorical definition of narrative according to James Phelan and Rabinowitz, it has not received as much theoretical attention as the other components. Each narrator's occasion has clear temporal dimensions that cannot be reduced to one side of the story/discourse binary. Although many postclassical schools of narratology have moved away from the story/discourse distinction as central to the definition of narrative, theorists still use this binary when describing narrative order. This pair of terms fails to capture a crucial dimension of narrative time: telling time, the story moments from which a narrator speaks that contain every word of their text. While the Great House's text order ending leads to a depressing conclusion, the novel's telling order and simultaneous telling times illuminate more optimistic interpretations. Attending to the dimension of telling time in Great House illuminates an intersubjective field that not only makes the novel's tragic themes bearable but also imbues the novel with a sense of vitality and hope.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".