Building new cities in the Global South: Neoliberal planning and its adverse consequences
Bibliographic record
Abstract
Mushrooming new city developments have occurred in many developing countries in the past two decades, which generate profound socio-economic, environmental, and politico-institutional consequences. This article examines how neoliberal planning facilitates and shapes new city projects in the Global South. In particular, this article focuses on three key mechanisms of neoliberal planning that promote new cities: deregulation, authoritarian state intervention, and public-private partnerships (PPPs). The findings suggest that neoliberal planning has been widely employed as a critical tool in developing new cities across the Global South, which has generated detrimental consequences such as social exclusion and inequality, spatial fragmentation, and environmental deterioration. It also finds that deregulation, authoritarian state intervention, and PPPs can work hand in hand in complex and dynamic ways to foster new cities. Although this article does not argue that neoliberal planning is the only mechanism that facilitates new city building, pervasive and variegated neoliberal planning warrants further attention to the interactions between neoliberal planning and new city building. In addition, further research is needed to decipher how neoliberal planning coexists and interacts with non-neoliberal ideologies and practices in new city developments, which is critical for promoting sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".