Bibliographic record
Abstract
Introduction In 2017, I was invited to speak at a workshop in Calgary, Canada, on the African smart city. I found this to be a curiously ill-defined task given the size and diversity of the continent. The central message I hoped to convey was that the manifestation of digital technologies is intrinsically connected to people’s livelihood strategies. What distinguishes African cities, if one is to generalize, are a number of features that colour the incorporation of information and communication technology (ICT) into city processes: informality, crumbling infrastructure and increasing urban poverty. These facts are not surprising, and this is not a new argument, but I nevertheless experienced some challenges to my presentation. My choice of projected images of street vendors using mobile phones and billboards promoting ubiquitous connectivity juxtaposed with immediate city surroundings showing dilapidated road infrastructure was an uncomfortable contrast to the smart city imaginary. Corporate displays of smart cities in Africa are eerie in their similarities: tall, glass-clad skyscrapers interspersed with wide avenues and slick inhabitants glued to their mobile phones. They portray a strange ‘placeless-ness’ that could be Dubai, Singapore or Seoul. Regardless of the imaginaries that inspire them, they bear very little resemblance to the ‘real’ city, in Africa or elsewhere. At the same event, a colleague remarked on how my hometown, Cape Town, had a mythical quality to it, a Shangri-La of sorts: beautiful and historically and geographically compelling, perched on the Southern tip of Africa and thus geographically remote enough to reinforce this fantasy. I was reminded of yet another compelling narrative: Cape Town as the ‘Silicon Cape’, the ‘Investment Connection into Africa’ displayed on a billboard at Cape International Airport. It struck me that my research included many aspirations: the smart city, the connected citizen and, of course, innovation as central to African livelihoods. I appear to peddle in fanciful ideas, but I believe it imperative to probe them and confront the contradictions contained therein. The relationship between technology and social development has been subject to important areas of criticism that feature in a diverse range of disciplines: urban studies, development studies and urban geography are among them.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".