Gender gaps in the path to adulthood for young females and males in six African countries from the 1990s to the 2010s
Bibliographic record
Abstract
Abstract In this paper, we study on a comparative basis the school-to-work transition of young women and young men in six countries in sub-Saharan Africa, and we examine how this has evolved over recent years, based on the data collected by Demographic and Health Surveys. We examine educational attainments and the nature of early jobs young people are able to obtain, as well as considering their relationship to marriage and fertility outcomes, factors which are likely to be particularly relevant for young women. A pooled regression analysis shows that educational levels have increased substantially and gender gaps have narrowed in most countries. Access to better jobs has improved much more slowly with unchanging gender gaps in most countries, so that agriculture is still the dominant sector of employment for most young men and women. We model correlates of key educational outcomes and access to different types of jobs those controlling for individual- and household-level characteristics, including marital status, presence of children and wealth. Attaining a high level of education is unsurprisingly critical for access to the best jobs and is also associated with young women delaying marriage and childbearing.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".