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Record W4312189710 · doi:10.22146/jh.69020

Theorizing Beauty Regimes: Indonesian Women Performing their Gender Ideology and Resistance through Makeup

2022· article· en· W4312189710 on OpenAlexfundno aff
Suzie Handajani

Bibliographic record

VenueJurnal Humaniora · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversitetet i AgderTemple UniversityWilfrid Laurier UniversityUniversitas Gadjah MadaUniversity of Oxford
KeywordsBeautySubversionSociologyAestheticsIdeologyIndonesianAgency (philosophy)Deconstruction (building)Gender studiesFemininityPoliticsSocial sciencePolitical scienceArtLawPhilosophy

Abstract

fetched live from OpenAlex

This article is about how Indonesian women talk about their beauty practices. They are aware how their beauty routines are often seen as banal and shallow but simultaneously essential to their gendered beings. However, this article argues that women are able to subvert the deprecating narratives of their beauty regimes into empowering ones while maintaining the same practices. Through their practices, they seem to conform to the beauty requirement in society. However, through their discourse, they present their beauty regimes with perspectives that put their free will and agency at the centre of their beauty regimes. The research used a sample of twenty-two Indonesian women aged from the mid-twenties to mid-sixties, to ask about beauty routines. Their answers are analyzed by using feminist discourse analysis to seek possibilities of subversion and empowerment. Another theoretical approach used in this research is the politics of everyday lives. The problematization of everyday practices allows for the deconstruction of ideology that perpetuates gendered norms of beauty. This research is significant because it provides a blueprint for further research on gender politics in the 21st century that focuses on everyday practices.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.294
Teacher spread0.251 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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