Bibliographic record
Abstract
FOREWORD Richard Florida 8 INTRODUCTION Paul Knox 10 THE FOUNDATIONAL CITY Lily Leontidou, Guido Martinotti 16 Core cities Athens and Rome Secondary cities Knossos, Santorini, Sparta, Pella, Syracuse, Marseille, Alexandria, Constantinople, Babylon THE NETWORKED CITY Raf Verbruggen, Michael Hoyler, Peter Taylor 34 Core cities Augsburg, London, Venice, Florence, Innsbruck, Lubeck, Bruges, Paris, Ghent THE IMPERIAL CITY Asil Ceylan Oner 52 Core city Istanbul Secondary cities Rome, St. Petersburg, Vienna, London, Beijing THE INDUSTRIAL CITY Jane Clossick 70 Core city Manchester Secondary cities Berlin, Chicago, Detroit, Dusseldorf, Glasgow, Sheffield THE RATIONAL CITY Andrew Herod 88 Core city Paris Secondary cities Vienna, New York, London, Budapest, Washington, D.C. THE GLOBAL CITY Ben Derudder, Peter Taylor, Michael Hoyler, Frank Witlox 106 Core cities London and New York Secondary cities Frankfurt, San Francisco, Geneva, Mumbai, Nairobi THE CELEBRITY CITY Elizabeth Currid-Halkett 124 Core city Los Angeles Secondary cities New York, London, Milan, Mumbai, Las Vegas THE MEGACITY Jan Nijman, Michael Shin 140 Core city Mumbai Seconday cities Cairo, Mexico City, Jakarta, Karachi, Shanghai, Sao Paulo, New York THE INSTANT CITY Lucia Cony-Cidade 158 Core city Brasilia Secondary cities Abuja, Chandigarh, Canberra THE TRANSNATIONAL CITY Jan Nijman, Michael Shin 176 Core city Miami Secondary cities Vancouver, Hong Kong, Dubai, Singapore, Dublin, Los Angeles THE CREATIVE CITY Paul Knox 194 Core city Milan Secondary cities Paris, New York, London, Portland, Los Angeles THE GREEN CITY Heike Mayer 210 Core city Freiburg Secondary cities Stockholm, Portland, Curitiba, Masdar City, Gussing, Wildpoldsried THE INTELLIGENT CITY Kevin C. Desouza 226 Core city London Secondary cities Amsterdam, Tokyo, New York, Singapore, Seoul, San Francisco, Chicago, Sydney, Vienna APPENDICES Glossary 224 Resources 246 Contributors 250 Index 252 Acknowledgements 256
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.338 | 0.263 |
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".