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Record W3125004687 · doi:10.5539/jel.v10n1p94

The Making of a Good Woman: Why Do Pre-School Girls in the KSA Have to Navigate Two Different Worlds to Survive Socially?

2021· article· en· W3125004687 on OpenAlexvenueno aff
Mona Al zahrani

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFemininityGirlBeautyIdentity (music)PsychologyGender studiesSocial psychologySociologyGender roleIdeologyDevelopmental psychologyAestheticsPolitical science

Abstract

fetched live from OpenAlex

The article discusses how young females navigate and develop a solid sense of two worlds in order to be perceived a ‘good girl’ that can be positioned within the society and maintain the female gender identity that is expected of them in the future. One world is where they are expected to show all the attributes of femininity and beauty and the other world is where they are required to develop a strong sense of ‘self-control’, to be ‘a good girl’ who complies with societal confinements and restrictions on their female body and mobility. This article has emerged from a doctorate research entitled: The Making of a Good Woman: Analysing children’s narratives on female gender identity and role in pre-school Saudi Arabia. It was a study into how female gender identity is constructed in the Kingdom of Saudi Arabia (KSA) by analysing children’s (young girls 4-6 years) perspectives within pre-school, exploring their perceptions of female identity and role in the KSA. Exploring the ways in which gender identities were interpreted and manifested; studying the influences, apparent ideologies and discourses that affect female gender construction. Through the analysis of the data, interesting results emerged that exposed the consideration of gender roles, permissible and non-permissible behaviour and attitudes, and the realisation that female gender is often constructed, in the KSA, through fear and restrictions.

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)0.001
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 score0.073
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.024
GPT teacher head0.370
Teacher spread0.346 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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