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Record W2914373906 · doi:10.5539/ells.v9n1p114

Is Julian Barnes Reliable in Narrating the Noise of Time?

2019· article· en· W2914373906 on OpenAlexvenueno aff
Michael H. M. Ng

Bibliographic record

VenueEnglish Language and Literature Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicNarrative Theory and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBattleNarrativeBiographySociologyArt historyPhilosophyLiteratureArtHistory

Abstract

fetched live from OpenAlex

Wayne C. Booth says that a novelist creates an implied author that is an ideal, literary, and created version of the real author. Seymour Chatman has emphasized the implied author is a principle that invents the narrator who has the direct means of communicating. Chatman says it is important distinguish among narrator, implied author, and real author. Booth originally says that unreliable narrators vary on how far and in what direction they depart from the author’s norms. The concept of Booth’s term ‘unreliable narrator’ has been a subject to debate. In Ansgar Nunning’s perspective, the reader has a role in detecting narrational unreliability. There are four forms of unreliable narration: intranarrational unreliability, internarrational unreliability, intertextual unreliability, and extratextual unreliability. Julian Barnes’ novel The Noise of Time is a fictional biography of a real Russian composer named Dmitri Shostakovich whose work of art flourishes even under the oppression of the Soviet government. According to a review in The Guardian, the novel is mainly on Shostakovich’s battle with his conscience when living under the rule of Joseph Stalin. It is possible that the real author, implied author, and narrator are the same person in Barnes’ case. The objective of this article is to examine whether Barnes is reliable in telling the story of Shostakovich or not.

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)0.012
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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