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Record W2919943986 · doi:10.5539/ijel.v9n2p321

Representation of Gender Through Framing: A Critical Discourse Analysis of Hillary Clinton’s Selected Speeches

2019· article· en· W2919943986 on OpenAlexvenueno aff
Safina Kanwal, María Isabel Maldonado García

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCritical discourse analysisSociologyDiscourse analysisIdeologyFraming (construction)Frame analysisPower (physics)Identity (music)Representation (politics)Gender studiesMedia studiesPoliticsLinguisticsContent analysisSocial sciencePolitical scienceAestheticsLaw

Abstract

fetched live from OpenAlex

Foucault’s theory of power and discourse has opened new horizons in the various fields of linguistics. It has brought the working of the power of discourse into the focus of research. Critical Discourse Analysis looks at this relationship between language and power. Language is taken as a patent tool for exerting power and for building identity (Foucault, 1998). Critical discourse analysis (CDA) reveals the ways by which discourse is manipulated for the construction of various domains such as identity, ethnicity, ideology, cultural differences and gender. The most wide-ranging and most influential work in CDA is of Norman Fairclough. He takes language as a social practice. He makes it clear that the power of discourse is used for depiction of ideology and gender representation. The present study used Critical Discourse Analysis (CDA) as an approach to find out the working of frames for representation of gender identity. The current study analyzed the campaign speeches of Hillary Clinton for finding out her projection of gender identity through frames. The data of the study consists of her opening primary campaign speech which is the Campaign Launch Speech and her last speech for Primary campaign that was delivered in the American presidential election of 2016. The theoretical framework for the present study is Fairclough’s Three Dimensional Model (2015) and the tool applied on this model for looking into the working of frames is the Frame Problem Tool of Gee (2014). The results of the study revealed that Hillary used the technique of framing for projecting her gender identity. She used the fight and family frames for the modification of the boundaries of American presidency with respect to gender. Through her political discourse she framed herself as a brave and bold woman who had she become the president of the United States would have fought for the rights of all Americans irrespective of their creed, sect, religion, gender and nationality.

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)0.012
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.004
Science and technology studies0.0130.018
Scholarly communication0.0100.005
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.361
Teacher spread0.321 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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