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Record W4404366789 · doi:10.5539/ells.v14n4p29

Feminism Between the Written and the Spoken Word

2024· article· en· W4404366789 on OpenAlexvenueno aff
Rabab Abdelfattah

Bibliographic record

VenueEnglish Language and Literature Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicPoetry Analysis and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsFeminismWord (group theory)Computer scienceLinguisticsNatural language processingArtificial intelligenceSociologyGender studiesPhilosophy

Abstract

fetched live from OpenAlex

By evaluating the audience’s reaction to and involvement with feminism themes offered in both page poetry and spoken word poetry, the author attempts to determine which is best suited to reflect on these concerns, and what makes one a better communicative medium than the other. Both poetics and hermeneutics theories will be utilized to trace these variations in order to conduct an in-depth investigation of the work’s structures, form, and aesthetic qualities that would have particular impacts on the reader. The theories used will also allow the author to analyze the works under study’s verbal and nonverbal signals. Maya Angelou, Audre Lorde, and Jackie Hill Perry are the poets chosen for this research. They were chosen primarily for their reputation as feminists and civil rights activists, as well as for their fame in the realm of poetry. The three were well-known poets, yet their material took on distinct forms. The study reveals that, while page poetry excels in language, musical features, and more in-depth and original images, it does not thrive in public-opinion issues. When it comes to audience recognition and interaction, spoken word poetry is the most intimate and effective at generating a response, especially when it comes to women's issues. The paper's ultimate purpose is to have addressed a research gap in appraising spoken word poetry in this specific subject of critical themes.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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.

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

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