Bibliographic record
Abstract
Abstract This book aims to shed light on the field of contemporary literature by offering a definitive theory of the constellational novel. By the constellational novel, I mean novels that have an associative, essayistic, digressive, and densely patterned prose form. These novels are recognizable by the presence of a first-person narrator committed to drawing affinities and making connections among disparate things. Beginning with Marcel Proust, my argument focuses on novels published over roughly the last two decades (between 2001 and 2020) by writers such as W. G. Sebald, Lisa Robertson, Teju Cole, Jacqueline Rose, and Olga Tokarczuk. Strikingly, it is often assumed that the attunement of their narrators to an unfolding web of potential interconnections holds an ethical promise of new ways of relating to oneself, others, and the world. I consider this implication of ethics and associative form to be peculiar and, in some important respects, unprecedented in the history of the novel. How is recognizing connections between things ethical, exactly? Could it not simply be the working of a resourceful or possibly even deranged intelligence, one that obsessively sees patterns everywhere? Why should the value of literature hinge on such an idiosyncratic process? And what does finding affinities have to do with the more familiar categories of novelistic form, like character and narrative? Taking inspiration from the work of Walter Benjamin, this book analyzes the distinctive ethics of affinity offered by these novels, and thus seeks to clarify one of the most intriguing and consequential developments in the contemporary novel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".