Bibliographic record
Abstract
List of Illustrations vii Notes on Contributors viii Series Editors Preface xiii Preface and Acknowledgments xv Introduction Worlding Cities, or the Art of Being Global 1 Aihwa Ong Part I Modeling 27 1 Singapore as Model: Planning Innovations, Knowledge Experts 29 Chua Beng Huat 2 Urban Modeling and Contemporary Technologies of City-Building in China: The Production of Regimes of Green Urbanisms 55 Lisa Hoffman 3 Planning Privatopolis: Representation and Contestation in the Development of Urban Integrated Mega-Projects 77 Gavin Shatkin 4 Ecological Urbanization: Calculating Value in an Age of Global Climate Change 98 Shannon May Part II Inter-Referencing 127 5 Retuning a Provincialized Middle Class in Asia's Urban Postmodern: The Case of Hong Kong 129 Helen F. Siu 6 Cracks in the Facade: Landscapes of Hope and Desire in Dubai 160 Chad Haines 7 Asia in the Mix: Urban Form and Global Mobilities Hong Kong, Vancouver, Dubai 182 Glen Lowry and Eugene McCann 8 Hyperbuilding: Spectacle, Speculation, and the Hyperspace of Sovereignty 205 Aihwa Ong Part III New Solidarities 227 9 Speculating on the Next World City 229 Michael Goldman 10 The Blockade of the World-Class City: Dialectical Images of Indian Urbanism 259 Ananya Roy 11 Rule by Aesthetics: World-Class City Making in Delhi 279 D. Asher Ghertner Conclusion Postcolonial Urbanism: Speed, Hysteria, Mass Dreams 307 Ananya Roy Index 336
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.665 | 0.473 |
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".