Science, Technology, Engineering and Mathematics (STEM): Liberating Women in the Middle East
Bibliographic record
Abstract
Middle East Region is home to more than 400 million people, representing 5% of world population, and boasts aworkforce of 103 million scattered across 22 countries (Lord, 2016). Sixty five percent of the populations are youngaged 25 or under, which puts growing stress on educational, health and social systems. Over the last decade, mostMiddle East countries put into action many reforms for women’s rights and sensitivity toward gender issues. Currently,almost all Middle East countries have ratified the Convention on the Elimination of all Forms of Discrimination againstWomen (CEDAW). Many nations in the Region shown strong commitment to uplift education and make themaccessible to all eligible women. There was also substantial increase in the allocation of funds for education in nearlyall Middle East nations. For a balanced national development, women are needed in the various areas where theirfunctions are most suitable. In principle, there are equal opportunities for both genders but social perception andprejudice determine which types of employment are particularly suitable for women or men. Several renowned MiddleEastern women are Physicians, Chemist, Physicist, Engineers, Doctors, Judges, Lawyers, Journalist, Poets, Novelistand even Legislatives (Islam, 2017)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".