When open data and data activism meet: An analysis of civic participation in Cape Town, South Africa
Bibliographic record
Abstract
Municipal open data projects are motivated by a desire to democratize data access and knowledge production, strengthen transparency, and advance cities socially and economically. However, their effects and implications are insufficiently analyzed. This paper examines civic engagement in open data in Cape Town, South Africa, the continent's first municipal‐level open data initiative. Findings reveal how local civil society organizations have been driving engagement with municipal open data as part of their recent turn towards technology and data‐driven forms of public engagement and activism. This analysis highlights the important role of the “smart civil society organization”—occupying a position between the smart city and smart citizen—that is developing significant capacity to produce and share data about the city's informal settlements with stakeholders in government, the private sector, and wider society. Minimal engagement with or recognition of civil society efforts illustrates the limits to the city's philosophy of data openness, which is largely restricted to releasing selected government datasets to the public. The notion of “bi‐directional open data” is developed here to characterize emerging possibilities for data openness between governments and the public. This may be particularly relevant for cities like Cape Town with a highly active, capable, and data‐literate civil society.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".