Bibliographic record
Abstract
How are metropolitan regions governed? What makes some regions more effective than others in managing policies that cross local jurisdictional boundaries? Political coordination among municipal governments is necessary to attract investment, rapid and efficient public transit systems, and to sustain cultural infrastructure in metropolitan regions. In this era of fragmented authority, local governments alone rarely possess the capacity to address these policy issues alone. This book explores the sources and barriers to cooperation and metropolitan policy making. It combines different streams of scholarship on regional governance to explain how and why metropolitan partnerships emerge and flourish in some places and fail to in others. It systematically tests this theory in the Frankfurt and Rhein-Neckar regions of Germany and the Toronto and Waterloo regions in Canada. Discovering that existing theories of metropolitan collective action based on institutions and opportunities are inconsistent, the author proposes a new theory of "civic capital", which argues that civic engagement and leadership at the regional scale can be important catalysts to metropolitan cooperation. The extent to which the actors hold a shared image of the metropolis and engage at that scale strongly influences the degree to which local authorities will be willing and able to coordinate policies for the collective development of the region. Metropolitan Governance and Policy will be of interest to students and scholars of comparative urban and metropolitan governance and sociology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.014 |
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".