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The Constellational Novel

2025· book· en· W4412930542 on OpenAlexaff
Louis Klee

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicLiterature and Cultural Memory
Canadian institutionsTrinity College
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.491
Threshold uncertainty score0.993

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)0.0080.000

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 head0.024
GPT teacher head0.203
Teacher spread0.179 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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