Meeting the Infrastructure Challenges of African Cities
Bibliographic record
Abstract
Today, less than 10% of the world's urban population lives in African cities. By the end of this century that ratio is on track to swell to more than a third of the world's total (a growth of 2.2 billion urban residents). Today, just seven of the world's 100 largest cities are in Africa, by 2050 that will increase to 21, and by 2100, 40 of the world's 100 largest cities are expected to be in Africa and five of the world's largest 10 cities will be in Africa, each with more than 50 million residents. In addition to the $20 trillion needed over the next 40 years to build the cities for more than 2 billion people, by 2040 Africa also needs the equivalent of about 600,000 engineer graduates per year to design and manage the services underpinning these cities. When assessing Africa's urbanization trends and the acute need for finance, stability of macroeconomic and social conditions, institutional strengthening, efficient urban form, and capacity - this paper asserts that a critical need is capacity, especially domestic engineering capacity. The scale of Africa's capacity needs will necessitate new models of collaboration and urban management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".