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Record W3183324155 · doi:10.5539/ells.v11n3p28

A Study on the Postmodern Narrative Features in Toni Morrison’s Song of Solomon

2021· article· en· W3183324155 on OpenAlexvenueno aff
Wang Miaomiao, Chengqi Liu

Bibliographic record

VenueEnglish Language and Literature Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesMinistry of Education of the People's Republic of China
KeywordsPostmodernismMetafictionNarrativeIndeterminacy (philosophy)LiteratureAestheticsHistorySociologyPhilosophyArtEpistemology

Abstract

fetched live from OpenAlex

Toni Morrison (1931-2019) is renowned as the Nobel and Pulitzer Prize-winning American novelist. Her third novel Song of Solomon was written in the context of postmodernism, which embodies a variety of postmodern narrative features. Postmodern works are frequently inclined to ambiguity, anarchism, collage, discontinuity, fragmentation, indeterminacy, metafiction, montage, parody, and pluralism. Such postmodern narrative features as parody, metafiction and indeterminacy have been manifested in Song of Solomon. In this novel, Toni Morrison employs the strategy of parody in order to subvert traditional narrative modes and overthrow the western biblical narrative as well as African mythic structure. Meta-narratives are also used in the text to dissolve the authority of the omniscient and omnipotent narrator. By questioning and criticizing the traditional narrative conventions, Morrison creates a fictional world with durative indeterminacy and unanswered problems. Through presenting parody, metafiction and indeterminacy, this paper attempts to analyze the postmodern narrative features in Song of Solomon and further explore Morrison’s writing on the African-American community and its future development.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.518

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)0.0000.000

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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2021
Admission routes1
Has abstractyes

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