A Tale of Three Cities: The Rise of Dubai, Singapore, and Miami Compared
Bibliographic record
Abstract
The literature on “global cities”, following a publication by Saskia Sassen of a book under the same title, has focused on those prime centers of the capitalist economy that concentrate on command-and-control functions in finance and trade worldwide. New York, London, Tokyo, and sometimes, Frankfurt and Paris are commonly cited as such centers. In recent years, however, another set of mercantile and financial centers have arisen. They reproduce, on a regional basis, the features and functions of the prime global cities. Dubai, Miami, and Singapore have emerged during the first quarter of the XXI century as such new regional centers. This paper explores the history of the three; the mechanisms that guided their ascent to their present position; and the pitfalls—political and ecological—that may compromise their present success. The rise of these new global cities from a position of insignificance is primarily a political story, but the stages that the story followed and the key participants in it are quite different. A systematic comparison of the three cities offer a number of lessons for urban scholarship and development policies. Such lessons are supplemented by the experiences of other cities that attempted to achieve or sustain a similar global status but failed, for various reasons, to do so. Such experiences are also discussed in the conclusion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".