Bibliographic record
Abstract
This thesis investigates the role of education achievement in explaining the growth experiences of countries in sub-Saharan Africa (SSA).Chapter one presents a new regional measure of educational achievement for SSA developed from three large-scale assessments: Le Programme d'Analyse des Systèmes Educatifs de la CONFEMEN, the Southern and Eastern Africa Consortium for Monitoring Educational Quality and the Monitoring Learning Project.Chapter one also presents a cross-country macro-level analysis of the determinants of educational achievement.The results suggest that family factors (proxied using the adult literacy rate and GDP per capita) and school factors (proxied using the percentage of trained teachers) play a role in explaining educational achievement at the primary level in SSA but the magnitude of each effect is dependent on assessment subject.Chapter two presents a micro-level case study of the determinants of educational achievement.Cameroon is selected for this case study as it is identified as one of the outliers in Chapter one.The results of the analysis suggest that initial performance and included student-, school-, and class-factors are important determinants of educational outcomes but each effect is nuanced and varies by education sector (Anglophone or Francophone), school location (rural area or urban area) and/or assessment subject (numeracy or literacy).The results also provide evidence of the complexities associated with estimating determinants of educational achievement and show that local context as well as family and school characteristics matter.Also important are assessment subject, quality of the adopted proxies and interactions between the various explanatory factors.iii Chapter three investigates the effect of the educational quality measure developed in Chapter one on economic growth in SSA.The findings from the regional analyses are generally weak.However, the results of the global analysis provide evidence that lend credence to existing literature on the importance of education in promoting growth.The global results also provide corroborating evidence of the essential role of educational quality in promoting economic growth.Finally, the global results suggest that educational quality and educational quantity are distinct yet complementary components of human capital development and explain different aspects of the educational process.I am especially indebted to
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".