Making Mangaung Metro: The politics of metropolitan reform in a South African secondary city
Bibliographic record
Abstract
Metropolitan reforms, which include the creation of unified metropolitan governments through municipal mergers and reclassification, are emerging as one strategy to address planning and service delivery challenges in the wake of increasing urbanisation across sub-Saharan Africa. Although metropolitanisation adds service area and mandates, well-functioning secondary cities that are part of a two-tier governance system in South Africa are pursuing metropolitanisation. The case of Mangaung, an early instance of secondary city metropolitanisation, is an opportunity to examine the motivations underlying these reforms, the politics involved and their impacts on urban governance. Mangaung’s political and administrative leadership pursued metropolitanisation to jump scale, attain greater political autonomy vis-à-vis other tiers of government, and obtain fiscal and technical resources available only to metropolitan municipalities in South Africa’s urban municipal hierarchy. Metropolitanisation was no panacea for Mangaung’s governance challenges, however, since it did not resolve the underlying weaknesses in municipal capacity or the regional economy, nor did it address the spatial legacies of apartheid that produced a sprawling metropolitan service area. As other South African secondary cities contemplate metropolitanisation, we recommend revising municipal structures and mandates and strengthening administrative capacities and economies in secondary cities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".