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Record W4308798752 · doi:10.1515/css-2022-2079

Narrative identity of the possible author: a tertiary narrativization of Chinese realist art of the 1950s–1990s

2022· article· en· W4308798752 on OpenAlexaff
Lian Duan

Bibliographic record

VenueChinese Semiotic Studies · 2022
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsConcordia University
Fundersnot available
KeywordsNarrativeIdentity (music)NarratologyLiteratureAestheticsChinaNarrative criticismNarrative artNarrative historyHistorySociologyArtContemporary artArt historyPerformance art

Abstract

fetched live from OpenAlex

Abstract From a narratological perspective, this essay reinterprets the development of Chinese realist art under Western influence in the second half of the twentieth century and explores the narrative issue of the possible author that is transformed from the integral reader. As a crucial response to Western influence, realist art in China developed from imitating to appropriating Western art and continued from taking inspiration from Western art to participating in the international arena of conceptual art with certain renovations. In this essay, the narratological notion of “possible author” is proposed to discuss the issue of narrative identity. While some scholars declared the death of the reader, this essay introduces a new reader to art historical narrative. This is a tertiary reader that transforms into a possible author who re-narrates the story of art history in the possible space between the secondary narration and tertiary narrativization. In this space, the three layers of the first-hand fabula, secondary narration, and tertiary narrativization work together in defining the possible author’s narrative identity as re-interpretive and critical.

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)0.002
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 score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.011
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.002
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 head0.023
GPT teacher head0.320
Teacher spread0.297 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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