Sub-Sahara Africa’s Higher Education: Financing, Growth, and Em-ployment
Bibliographic record
Abstract
Although higher education plays a vital role in the socio-economic development of Sub-Saharan Africa, enrollment in universities in the region is unexpectedly low compared to other regions. However, Sub-Saharan African countries have made strides in increasing access to higher education amidst constraints and challenges. The efforts have led to increases in enrollment and what many countries did not anticipate is the increase in unemployment from the greater output of students. In this study, we use panel data from eleven Sub-Saharan African countries for 2000-2018 to analyze the relationship between higher education and unemployment. A panel fixed effect model was estimated, and the results indicate that unemployment has a negative and significant effect on higher enrollment. Besides, higher education enrollment has a significant but negative effect on employment. Per capita income significantly affects enrollment into higher education and has the expected sign. The estimates further show that government expenditures on higher education play a significant role in the demand for places in higher education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".