On the lack of women researchers in the Middle East and North Africa
Bibliographic record
Abstract
Abstract Recent gender policies in the Middle East and North Africa (MENA) region have improved legal equality for women with noticeable effects in some countries. The implications of these policies on science, however, are not well-understood. This study applies a bibliometric lens to describe the landscape of gender disparities in scientific research in MENA. Specifically, we examine 1.7 million papers indexed in the Web of Science published by 1.1 million authors from MENA between 2008 and 2020. We used bibliometric indicators to analyze potential disparities between men and women in the share of authors, research productivity, and seniority in authorship. The results show that gender parity is far from being achieved in MENA. Overall, men authors obtain higher representation, research productivity, and seniority. But some countries stand out: Tunisia, Lebanon, Turkey, Algeria and Egypt have higher shares of women researchers compared to the rest of MENA countries. The UAE, Qatar, and Jordan have shown progress in terms of women participation in science, but Saudi Arabia lags behind. We find that women are more likely to stop publishing than men and that men publish on average between 11 and 51% more than women, with this gap increasing over time. Finally, men, on average, achieved senior positions in authorship faster than women. Our longitudinal study contributes to a better understanding of gender disparities in science in MENA which is catching up in terms of policy engagement and women representation. However, the results suggest that the effects of the policy changes have yet to materialize into distinct improvements in women’s participation and performance in science.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".