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Record W2801261072 · doi:10.5539/ass.v14n5p40

The Trace of Masculinity in Indonesian Women Politicians Campaign Speech

2018· article· en· W2801261072 on OpenAlexvenueno aff
Sri Suciati, Rustono Rustono, Teguh Supriyanto, Mimi Mulyani

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianSentenceTRACE (psycholinguistics)AdjectiveEuphemismPoliticsLinguisticsCritical discourse analysisMasculinityPsychologySubject (documents)SociologyGender studiesSocial psychologyPolitical scienceComputer scienceLawNounIdeology

Abstract

fetched live from OpenAlex

The research was based on the importance of greater involvement of women in the management of the State. This research aims to describe masculine female politician Indonesia campaign speech. In this study used a qualitative approach in the form of Critical Discourse Analysis (CDA) centred on the dismantling of the ulterior motive behind the language that is used to find the real message. The analysis was done to show the representation of the subject. Based on the results of research on speech discourse campaign prospective of Candidate Regent Kutai Kartanegara namely Rita Widyasari found that when women are involved in politics and do the speech of the campaign to attract sympathy glacial, the choice of the language used in speech tends to be masculine. In short, there was a campaign speech masculine woman through adjective apply, the question directly with the affirmative imperative sentence, constructions, the reference quantity (number), the sentence is active, and the presence of herself with the first person singular. The masculine act of campaign speech is applied so that women who tend to be "tough" transformed into a recognized figure of leadership because that assertiveness and straightforwardness in the language used.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.006
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 head0.018
GPT teacher head0.290
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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