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Record W3034064782 · doi:10.3968/11566

Sarcastic Feminism: A Lexico-Syntactic Analysis of Judy Syfers’ I Want A Wife

2020· article· en· W3034064782 on OpenAlexvenueno aff
James Boaner Olusaanu, Folorunso Oloruntobi

Bibliographic record

VenueStudies in literature and language · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWifeSarcasmLexisPower (physics)Context (archaeology)SyntaxFeminismSociologyGender studiesPsychologyLinguisticsLawHistoryPolitical sciencePhilosophyIrony

Abstract

fetched live from OpenAlex

This work examines the relationship between language and the plights of women as espoused by Judy Syfers in her text “I Want a Wife”. It seeks to establish the concerns of the writer and the choices she made in her agitation and struggle for women liberation. To achieve this, Feminist CDA approach was adopted in order to critically identify the implications of the writer’s lexical items within the context of her language. This would enable us demystify her language and see how gender power is constructed; see where women are placed on the ladder of power and the effort the writer makes to ameliorate the social status of women. Thus, attention was given to lexis and syntax (noun phrase). This helped us to find out that the context in which the writer agitates for women liberation is within the family and its attendant responsibilities. It was discovered through the examination of syntax of the language that the use of sarcasm and rankshifting were paramount. Through rankshifting, the writer presented the enormity of women’s plights, and condemns such man-made, imposed and killing plights using sarcasm.

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 score0.239
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.048
GPT teacher head0.322
Teacher spread0.274 · 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
Published2020
Admission routes1
Has abstractyes

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