Local Officials and the Struggle to Transform Cities
Bibliographic record
Abstract
Why are even progressive local authorities with the ‘will to improve’ seldom able to change cities? Why does it seem almost impossible to redress spatial inequalities, deliver and maintain basic services, elevate impoverished areas and protect the marginalised communities? Why do municipalities in the Global South refuse to work with prevailing social informalities, and resort instead to interventions that are known to displace and aggravate the very issues they aim to address? Local Officials and the Struggle to Transform Cities analyses these challenges in South African cities, where the brief post-apartheid moment opened a window for progressive city government and made research into state practices both possible and necessary. In debate with other ‘progressive moments’ in large cities in Brazil, the USA and India, the book interrogates City officials’ practices. It considers the instruments they invent and negotiate to implement urban policies, the agency they develop and the constraints they navigate in governing unequal cities. This focus on actual officials’ practices is captured through first-hand experience, state ethnographies and engaged research. These reveal day-to-day practice that question generalised explanations of state failure in complex urban societies as essential malevolence, contextual weakness, corruption and inefficiency. It is hoped that opening the black box of the workings of state opens paths for the construction of progressive policies in contemporary 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".