Changes at the Top: A Cross-country Examination over the 20th Century of the Rise (and Fall) in Rank of the Top Cities in National Urban Hierarchies
Bibliographic record
Abstract
The paper documents the evolution of rank orders for cities at the top of national urban hierarchies (top 10 cities, where possible). Ranks for the year 2000 are compared with 1950 for 74 nations and with 1900 for 52 nations, covering 375 and 288 cities respectively. Rank correlations with the year 2000 are calculated for both years. The rank order of cities in Europe shows significantly less variation over time than those for the New World and developing nations, consistent with the view that urban hierarchies harden as they mature. Changes in rank at the very top (rank 1) are rare. Where they occur, such changes can often be traced to political events that alter the direction of trade or the city’s role as central place. The results provide evidence both for and against locational fundamentals and cumulative causation arguments. The entrenched advantages of the first big cities to emerge are undeniable; but ‘fundamentals’ can be undermined by political events and by technological change.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".