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Record W4406332594 · doi:10.17951/nh.2024.9.89-114

Scarcity Poetics: Christian Bök’s Eunoia and the Economics of Literary Value

2024· article· en· W4406332594 on OpenAlexaffabout
Kevin Kvas

Bibliographic record

VenueNew Horizons in English Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUniversity Challenges and Reforms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPoeticsScarcityValue (mathematics)EconomicsPhilosophyPositive economicsNeoclassical economicsLiteratureArtPoetryMicroeconomicsMathematics

Abstract

fetched live from OpenAlex

Christian Bök’s Canadian bestseller Eunoia (2001) is an ideal study in how the romantic notion of literary value actually abides economic theories. Eunoia is a collection of five prose-poems each written using only one vowel grapheme (A, E, I, O, or U). These arbitrary material production constraints work just like economic sanctions, artificially inflating the scarcity—and hence value—of the text, both commercially and literarily. Discourse analysis of surrounding debates reveals that detractions and praises alike abide the same basic supply-and-demand logic: e.g., economic theories of (relative) marginal utility, as applied by Lee Erickson in The Economy of Literary Forms (1996). Building on Erickson’s thesis of how publication media costs shaped literary form and content, on Mary Poovey’s history of literary value’s origin in economic value, and on other efforts to combine literary study with economics, this essay applies economic theory to a rare opportunity to generalize the relation of literary material, form, and content. Conclusively, scarcity poetics/tactics are not just unique to Eunoia, but fundamental to all literary form. Eunoia’s extreme manipulation of linguistic materials isolates the general mechanism by which literary value is produced.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.033
Scholarly communication0.0090.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes2
Has abstractyes

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