Science, Technology, Engineering and Mathematics (STEM): Liberating Women in the Middle East
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it