New Spatial Logics in Global Cities Research: Networks, Flows and New Political Spaces
Bibliographic record
Abstract
Abstract While world cities or global cities research has become an established interdisciplinary field over a few short decades, criticisms have been levelled at its narrow focus on selected leading cities and specific economic activities of corporate headquarters, finance and advanced producer services. This article identifies four recent strands of research from geographers and urban scholars seeking more nuanced and critical approaches to processes of globalisation and urban development. First, there has been a shift away from examining urban hierarchies to exploring networks and flows between global cities and the adoption of a relational perspective. Second, there has been a re‐scaling of analysis to examine global city‐regions and global city‐states. Third, researchers have turned to ‘alternative’ global cities to reveal new nodes and networks that were often written off conventional accounts of the global cities map. Fourth, there has been greater emphasis on social inequality and urban politics within global cities and challenges for positive social change. The concluding section draws together some common themes than run through these research trajectories and highlights some future research agendas that capitalise on geographers’ sensitivity to place in studying global cities. This place‐based perspective appreciates the very differences that enable global cities to perform particular roles in the global urban network. It also recognises global cities as inhabited by real people and communities and the impetus for positive social change in the face of intraurban and interurban inequalities.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".