Mobility, Gender and Career Development in Higher Education: Results of a Multi-Country Survey of African Academic Scientists
Bibliographic record
Abstract
Empirical knowledge of the mobility of 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 report on an analysis of a subset of data from a multi-country survey, in order to address a lack of evidence on the geographic mobility of academic scientists in Africa, and how it relates to gender and career development. First, we compared women and men from 41 African countries in terms of their educational and work-related mobility, as well as their intention to be mobile. We further investigated these gendered patterns of mobility in terms domestic responsibilities, as well as the career-related variables of research output, international collaboration, and receipt of funding. Our focus then narrowed to only those women scientists who had recently been mobile, to provide insights on the benefits mobility offered them. The results are interpreted within a theoretical framework centered on patriarchy. Our findings lead us to challenge some conventional wisdoms, as well as recommend priorities for future research aimed at understanding, both theoretically and empirically, the mobility of women in the science systems of Africa, and the role it may play in their development as academic leaders in African higher education institutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".