Networks of European cities in worlds of global economic and environmental change
Bibliographic record
Abstract
Geographers use a variety of economic, social, and demographic data to measure the importance of global cities and the linkages between cities. We analyze the importance and connectedness of European cities using hyperlinks, or the electronic information provided by the Google Search engine. Hyperlinks are Web sites representing information that is produced; they are especially useful in measuring the impact of contemporary crises. We use the phrases economic slowdown and global financial crisis to derive a Global Financial Score (GFS) for 16 core, semiperiphery and peripheral European cities and global warming and climate change to derive a Global Environmental Score (GES). London and Paris are in the European core; Rome, Dublin, Madrid and Prague are in the semiperiphery; while Tallinn, Riga, and Belgrade are in the periphery. A strong positive relationship exists between the GES and GFS. We examine the linkages of the 16 cities to the 100 largest world cities and illustrate, with “clockgrams,” the linkages London, Brussels and Athens have with other world cities. We calculated the number of linkages each of the 16 cities had with other world cities to identify Europe’s urban cores, semiperipheries, peripheries, and deep peripheries. New York is in the core of both the economic and environmental maps. Some world cities are in the semiperiphery of one category and periphery of another. Milan, Istanbul, and Delhi are in the deep periphery for the GFS while Toronto and Athens are for the GES. Hyperlinks represent valuable databases to measure the impact of crises and regional and global urban linkages.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".