Towards political cohesion in metropolitan areas. An overview of governance models
Bibliographic record
Abstract
As cities grew beyond their administrative borders, the demand for metropolitan governance appeared. The last 50 years proved that there is no one, universal model of metropolitan governance as urban regions are very different all around the world. However, it seems quite obvious that if metropoles are to be the forefront of development, they need to provide a widely defined cohesion within their subordinate territories. Metropolitan political cohesion may be defined as a collaborative public governance which offers tailored managerial solutions for enhancing development based on the subsidiarity principle and the place‐based approach. Drawing on the lessons from major cities in North America and Europe: Copenhagen, Rotterdam, Stockholm, Hannover, London, Wrocław and Toronto the paper intends to dive into a few, selected cases of metropolitan government and the causes behind their failure and reappearance. How have various governments met the cardinal question of metropoles: to provide a resilient match between the functional urban region and the administrative structure? The answer to this question is not straightforward. Metropolitan authorities all over the world manage exceptionally complex systems, where the diversity of actors, complexity of relations and interdependences across an extended, fragmented and dynamic metropolitan region restrain governability. However some general trends in metropolitan governance may be outlined, regarding the recent history, main types of governance and legitimacy of metropolitan administration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".