What Are the Attitudes towards Changing Gender Roles within the Saudi Family?
Bibliographic record
Abstract
Saudi Vision 2030 was launched with a focus on three main themes: “a vibrant society,” “a thriving economy,” and “an ambitious nation” (2017). In order to realize these primary themes, more participation of women is required in the workforce. A number of legislative reforms have supported the entry of more women into the workforce, such as lifting the male guardian permission for work and travel, supporting programs for childcare, allowing women to drive cars, and ensuring women’s involvement in leadership positions. Saudi women are now working in defense, mining, construction, manufacturing, and retail among other sectors. As a result, women's participation in the workforce increased from 19.4% in 2017 to 35.6% in the second quarter of 2022. Because these social and economic transformations have the potential to influence the dynamics within the Saudi family, this study investigates the attitudes of Saudi men and women toward gender roles within the Saudi family. Based on the short version of the Attitudes toward Women Scale, an online questionnaire was sent to a random sample of 431 Saudis in the fall of 2022. Statistical analyses were conducted to measure women’s attitudes compared to men’s overall scores. Results of the independent sample t-test indicated that women held more pro-feminist attitudes compared to men.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".