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Record W3174730804 · doi:10.22148/001c.25273

Too isolated, too insular: American Literature and the World

2021· article· en· W3174730804 on OpenAlexvenueno aff
Matthew Wilkens

Bibliographic record

VenueJournal of Cultural Analytics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAmerican literatureHistoryIdentity (music)PoliticsNational identityPrincipal (computer security)Political scienceLiteratureLawAestheticsArt

Abstract

fetched live from OpenAlex

Are American authors homers? Do they devote too much of their attention to American concerns and settings? Is American literature as a whole different from other national literatures in its degree of self-interest? We attempt to answer these questions, and to address related issues of national literary identity, by examining the distribution of geo-graphic usage in more than 100,000 volumes of American, British, and other English-language fiction published between 1850 and 2009. We offer four principal findings: American literature consistently features greater domestic attention than does British literature; American literature is, nevertheless, significantly concerned with global loca-tions; politics and other international conflicts are meaningful drivers of changing literary attention in American and British fiction alike; and prize-nominated books are the only examined subclass of American fiction that has become significantly more international in the decades after World War II, a fact that may account for readers’ unfounded percep-tion of a similar overall shift in American literature.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.011
Science and technology studies0.0060.009
Scholarly communication0.0140.006
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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
GenreEmpirical

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

Citations7
Published2021
Admission routes1
Has abstractyes

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