Editorial: Women in chemistry 2022
Bibliographic record
Abstract
A wide plethora of research exists on women in STEM education, but research by female chemists in educational and employment settings is scarce, especially in the Middle East and North Africa region. Most current industries use computer-driven machinery, and rising levels of automation and automated control systems that are driven by machines have altered shop floor procedures. The necessary qualifications and skills for chemistry jobs have changed as a result. Chemistry education in Egypt is advancing. At least 15 STEM high schools operated by the Education Ministry have been established since 2011. In 2017, 43% of Egypt’s university STEM students were women which is higher than the percentage in the UK and all over the globe. The percentage increased to reach almost 47% in 2018/2019 as was reported in the Higher education bulletin from CAPMAS. The percentage of female graduates by field is shown in the 2021 UNESCO scientific report using data from 2018: agriculture 49.4%, engineering 20.9%, health & welfare 56%, natural sciences 64.2%, and ICT 36.8%. According to UNESCO data, in 2018 only 46% of Egypt’s total number of scientific researchers were women. This percentage exceeds the average percentage of female researchers in the Arab World which accounts for around 43% and that of EU which accounts for 34%. Women must engage in all fields in order for societies to advance and stay up with the times. However, in some fields, such as the male-dominated field of Mechanical and Electrical Engineering, there is very little female representation, so we are striving to raise awareness among youth in society. This editorial plays a small part in this by documenting women’s work in chemistry with all of its qualitative ramifications, highlighting successful research stories, and focusing on advancing and sustaining the presence in public and private sector institutions to emphasize the significance of women working in this profession and empowering them in the field of Chemistry. Diverse international representation of researchers have contributed to this Research Topic in the 2022 edition and all the manuscripts were published after a thorough peer review process. The following is a summary of the areas and contributions in each of these manuscripts organized in terms of broad research topics.
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.
How this classification was reachedexpand
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 | 0.004 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".