How national structures shape attitudes toward women’s right to employment in the Middle East
Bibliographic record
Abstract
Despite dramatic human development in recent decades, women’s employment rates in the Middle East and North Africa (MENA) region are the lowest in the world. Research shows that gender-egalitarian attitudes are key in explaining women’s employment. This study examines whether the Middle East stands out in terms of the degree to which individuals hold gender-egalitarian attitudes in the region and in terms of the factors that are most important in shaping attitudes toward women’s employment. I compare individual attitudes toward women’s right to employment in the MENA region to individual attitudes in a global selection of nations available in the fourth (1999–2004) wave of the World Values Survey (WVS) ( N = 57), using hierarchical linear models. I find that individuals in MENA hold significantly less egalitarian attitudes toward women’s employment, compared to those in all other nations sampled. There is not one variable (such as Islam or oil) that is key to explaining attitudes in the region. Instead, this negative regional effect is reduced by accounting for national religiosity, levels of female tertiary enrollment, shares of women in parliament, economic rights for women, and national economic development. However, the negative effect of being highly religious is magnified among those individuals living in MENA nations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".