On data cultures and the prehistories of smart urbanism in “Africa’s Digital City”
Bibliographic record
Abstract
Data is variably imagined and practiced according to values, behaviors, and norms fashioned over an extended temporal register, meaning data initiatives are not only influenced by contemporary technological and structural conditions, but also by the forces of history and culture. This claim is advanced by situating Cape Town’s smart city plans in a national historical context, highlighting how desires to be a “global city” driven by data, evidence, and openness come up against a data culture largely incompatible with these goals. A genealogy of South Africa’s politicized history of recordkeeping, biometrics, databases, and information sharing reveals the roots and legacy of an ambivalent data culture, which poses a considerable challenge to today’s data ambitions. Through this example, the paper makes two contributions to critical understandings of urban data. First, it advances the notion of data cultures – the values, behaviors, and norms ascribed to data by groups or organizations that together shape practices of data collection, management, use, and sharing. Second, it draws attention to the multi-scalar production of smart cities, when global data imaginaries meet national-scale characteristics at local places. These findings present a new lens for understanding the relative success or failure of (urban) data initiatives.
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".