Digital revolution in African cities: Exploring governance mechanisms to mitigate the societal impacts
Bibliographic record
Abstract
In an increasingly connected and digital African city it is crucial to identify the opportunities and challenges related to new technologies in cities to ensure that they do not create new inequalities and exclusions but contribute to the well-being of all. Governance is at the heart of this endeavour and local governments should put in place regulatory frameworks to ensure that one is left behind in African smart cities. Universal access to urban services driven by emerging technologies, the digital divide, digital inclusion, and digital rights, are all issues that emerge from the digital transformation of territories. As part of the African Cities Lab Summit 2023, young researchers met for a workshop lasting for an hour and a half on the impact of digital technology deployment in African cities. The aim was to analyze the impacts and societal challenges posed by the deployment of digital technologies in African cities in a local and global context and then to formulate recommendations for local governments. This paper summarizes the results of the discussions.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".