gender perspective on career challenges experienced by African scientists
Bibliographic record
Abstract
Empirical knowledge of the career challenges that confront African scientists, and women scientists in particular, holds an important key to achieving future success in the science systems of the continent. In this article, we address a lack of evidence generally on the careers of scientists in Africa, by providing the first continent-wide description of the challenges they face, and how these challenges differ between women and men. Our analysis of questionnaire-survey data on approximately 5000 African scientists from 30 countries shows that women are not more challenged than men by a variety of career-related issues, with the exception of balancing work and family, which the majority of women, regardless of age and region, experience. Contrary to expectations, women are not only less likely than men to report a lack of funding as having impacted negatively on their careers, but have been more successful at raising research funding in the health sciences, social sciences and humanities. These results, as well as those from a comparison of women according to age and region, are linked to existing scholarship, which leads us to recommend priorities for future interventions aimed at effectively ensuring the equal and productive participation of women in the science systems of Africa. These priorities are addressing women’s work–family role conflict; job security among younger women scientists; and women in North African and Western African countries. Significance: This study is the first to describe, on a multinational scale, the career challenges that confront African scientists, and women scientists in particular. Contrary to expectations, we found that African women scientists do not report experiencing career challenges to a larger extent than men do, and have been more successful at raising research funding in three of the six major scientific fields. However, the findings highlight the significance of the challenge that balancing work and family poses to the majority of African women scientists.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".