Arab Women in Science, Technology, Engineering and Mathematics Fields: The Way Forward
Bibliographic record
Abstract
In most countries of the world, 40 to 50 % of students are women. However, there is greater sex imbalance in STEMfields. Indicators show that tertiary education in Arab region is high compared with gender balance in severalcountries; there is even imbalance in favor of women as in Saudi Arabia & Gulf States.UNESCO and World Bank statistics reveal that Arab women actively pursuing STEM fields e.g. in 2014, womencomprises 59% of total students enrolled in computer Science in Saudi Arabia while UK and USA women enrolmentwere 16% and 14% respectively.Graduate women attempt to pursue career or postgraduate degrees are often excluded on bases of their gender andmarginalized therefore much less apt to enter and remain in the job, few achieve leadership positions.In principle, there are equal opportunities for both genders in many Arab States, but social perception and prejudicedetermine which types of employment are particularly suitable for women or men. Removing the barriers wouldfoster major social and economic benefits for every Arab State.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".