Urban China through the lens of neoliberalism: Is a conceptual twist enough?
Bibliographic record
Abstract
Neoliberalism as a hegemonic global ideology and framework of governance has been the subject of extensive critical analyses in geography and urban studies. Despite the conceptual difficulties involved, a growing number of scholars have attempted to apply this critical discourse to China. In this commentary, we critically interrogate the urban China literature that deploys the neoliberal lens, mostly authored by scholars outside China, and we raise the fundamental question as to whether this discourse can ever capture the central stories or trajectories of China’s urban transformation. We examine the interpretations of China’s urban land property market, urban inequality and its spatial manifestation, and the emerging urban governmentality – the areas in which neoliberalism has been most often invoked – to highlight the utility and limitations of a neoliberal treatment of China. We argue that the neoliberal representation of China’s urban (re)development, with its preoccupation with capital and class interests, is unable to effectively capture the distinctive nature of entanglement of capital, state and society in China, and thus obscures the driving role and the competing rationalities of the authoritarian state, and the rapid reconfiguration of urban society. By citing examples of recent urban China research, we show that the neoliberalism framework, even in its ‘variegated’ or ‘assemblage’ versions, tends to trap China’s analysis within a frame of reference comfortable to Western researchers, and ultimately hinders the development of diversified, potentially more fruitful inquiries of the urban world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".