The Making of a Good Woman: Why Do Pre-School Girls in the KSA Have to Navigate Two Different Worlds to Survive Socially?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".