A critical analysis of ‘smart cities’ as an urban development strategy in Africa
Bibliographic record
Abstract
Smart cities are becoming a popular urban development strategy to address complex and multiple challenges confronting cities globally, including in Africa. Using the 3RC framework, this paper critically analyses smart cities using experiences from Nairobi (Kenya), Johannesburg (South Africa), Lagos (Nigeria), Kigali (Rwanda) and Casablanca (Morocco). Are smart cities a panacea to Africa's quest for sustainable urbanization? Our analyses demonstrate that, if carefully planned and implemented, smart city interventions have the potential to transform the ways African cities are planned, managed, and governed. At the same time, smart city interventions in Africa are being implemented in contexts characterized by socio-economic inequalities, chaotic transport systems and massive governance failures among other challenges. We demonstrate that if ineffectively deployed, smart urban technologies might deepen existing inequalities and amplify spatial exclusion through privatization and marketization of urban space. Therefore, the adoption of smart city ideas in Africa must be rooted in contextual realities and properly calibrated to create urban spaces that are sustainable and inclusive.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".