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Record W2800229798 · doi:10.20381/ruor-21771

Unveiling Artists: Saudi Female Artists Life Stories

2018· dissertation· en· W2800229798 on OpenAlexfundno aff
Maha Alkhudair

Bibliographic record

VenueuO Research (University of Ottawa) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
FundersKing Saud UniversityUniversity of Ottawa
KeywordsArtVisual arts

Abstract

fetched live from OpenAlex

This study tells the life stories of four Saudi female artists. Using life story narrative approach, I focused on the following research questions: How are Saudi female artists fulfilling their aspirations as artists in the conservative Saudi society? What are the common and divergent themes in the life stories of the Saudi women artists, namely Safeya Binzagr, Maha Almalluh, Tagreed Albagshi, and Fida Alhussan? The artists were interviewed using open-ended questions and asked to discuss their artwork. The postmodern feminism and social construction theories were used to understand their life experiences and how they came to be “successful artists” in the conservative Saudi society. The findings showed that family and formal education played an important role in these women’s life journeys as artists. The Saudi society was also a major influence, sometimes supporting them, at other times obstructing them. These artists share many personality features such as being persistent, believing in themselves, taking risks, facing challenges, being independent, being responsible as artists and as part of society, and being honest in their artwork. This study contributes to the art education curriculum in Saudi schools and universities. Globally, it contributes to women’s studies and to social and cultural studies in shedding light on the Saudi society, especially as it is experienced by women.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.081
GPT teacher head0.382
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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