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Record W4391110057 · doi:10.33806/ijaes.v24i2.663

Unraveling the Multifaceted Narratives of Mixed-Race Identity in Natasha Trethewey's Bellocq’s Ophelia and Thrall

2024· article· en· W4391110057 on OpenAlexaboutno aff
Nehal Ali Abdulghaffar M. Kuraiem

Bibliographic record

VenueInternational Journal of Arabic-English Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryNarrativeIdentity (music)LiteratureGender studiesArtSociologyAesthetics

Abstract

fetched live from OpenAlex

Trethewey's poetry is an intervention into a worldwide debate about defining and socially restricting mixed-race identities; therefore, this research addresses these issues. In Gulfport, Mississippi, Trethewey was raised by relatives whose mixed-race marriage was unlawful. Her poetry has several allusions to both her dad, a writer, academic, and Canadian immigrant, and her mom, a caseworker. Trethewey's poetry intertwines the tale of her personal mixed ancestors with the racial history of America, even while combining this story with lyricism. "I'm capable of getting closer to the inner reality of a poem when the poetry leans towards the poetic," Trethewey remarked in her address. She used a poem entitled "Incident" from her Pulitzer Prize-winning book Native Guard as an instance. Her grandma sponsored a voting registration campaign for disadvantaged African Americans in the 1960s, and the Ku Klux Klan burned a symbol in her family's yard as a result. Trethewey reconstructed an early form of the poem to encapsulate the complete tale of the occurrence in the first four lines. This allowed her to utilize the rest of the poem to emphasize additional psychological realities. Unraveling the Multifaceted Narratives of Mixed-Race Identity in Natasha Trethewey's Bellocq’s Ophelia and Thrall is the ground upon which this study stands.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.072
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.342
Teacher spread0.311 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

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