Beyond Polycentricity: Does Stronger Integration Between Cities in Polycentric Urban Regions Improve Performance?
Bibliographic record
Abstract
Abstract A quarter of the European population lives in ‘polycentric urban regions’ (PURs): clusters of historically and administratively distinct but proximate and well‐connected cities of relatively similar size. This paper explores whether tighter integration can increase agglomeration benefits at the PUR‐level. We provide the first comprehensive list of European PURs (117 in total), establish their level of functional, institutional and cultural integration and measure whether this affects their performance. ‘Performance’ is defined as the extent to which urbanisation economies have developed, proxied by the presence of metropolitan functions. In this first‐ever cross‐sectional analysis of PURs we find that while there is evidence for all dimensions of integration having a positive effect, particularly functional integration has great significance. Regarding institutional integration, it appears that having some form of metropolitan co‐operation is more important than its exact shape. Theoretically, our results substantiate the assumption that networks may substitute for proximity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".