ARE AFRICA’S POOREST CHILDREN ON COURSE TO AVOID BEING LEFT BEHIND IN POVERTY BY 2030?
Bibliographic record
Abstract
This paper examines the performance of key factors influencing the prospect of Africa’s poorest children avoiding being left behind in poverty by 2030 as required by the United Nation’s Agenda for Sustainable Development, a set of sustainable development goals (SDGs) declared in 2015. At that time, sub-Saharan Africa (SSA) was facing both rising debt and deterioration of the fiscal space required to provide resources. Quantitative methods employing descriptive analysis on secondary data are used in this study to compare the trend of child poverty, as represented by under-5 mortality rates (U5MR), both over time and between country clusters. U5MR was chosen because it is an indicator of the well-being of a nation’s children. Countries were “clustered” into quartiles based on their average U5MR between 2000 and 2018. The results indicate marked disparities in U5MR across SSA. No strong association was found between economic growth and U5MR, but good governance, as demonstrated by progress towards achieving the SDGs, correlates with decreases in both U5MR and the incidence of childhood stunting. In the first U5MR quartile, the SDG index score is over 50% in all child poverty indicators under consideration, whereas in the fourth quartile it is below 50%. SSA as a whole performed well in child poverty indicators from 2000 to 2015; however, consideration of the period from 2015 to 2018 suggests that much remains to be done to lift every child out of poverty. Within and across countries, critical areas for immediate attention include: improving sanitation and access to clean water and lowering the prevalence of anaemia and stunting; increasing the rates of exclusive breastfeeding, birth registration, and pre-primary enrolment; and reducing youth unemployment and socioeconomic disparities. Cash transfers to low-income families may help address the added economic insecurity due to COVID‑19 that has left more children vulnerable to child marriage and child labour.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".