A critical analysis of ‘smart cities’ as an urban development strategy in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it