Metropolitan Transportation Institutions : Six Case Studies - Australia, Brazil, Canada, France, Germany, and the United States
Bibliographic record
Abstract
Transportation has always played a \n fundamental role in the formation of cities. Ports evolved \n where rivers flowed into the ocean or at the confluence of \n major rivers; sleepy outposts at the junction of major roads \n became bustling trading hubs. Although this relationship \n between transportation and development has been evident \n since the creation of the earliest urban societies, all \n previous conceptions of the city were made obsolete by the \n advent of the industrial revolution. The transportation \n challenges raised by this new city centered on congestion. \n Early forms of transit provided some relief, but as motor \n vehicles became common place, existing urban streets were \n overwhelmed. As roadways were enlarged and expressways \n constructed, the population of new suburbs expanded and the \n automobile became the dominant form of transportation in \n many developed cities. To address issues at this scale, \n cities and countries around the world have developed new \n institutions that sit between the scale of local and higher \n order governments. The example of Boston, presented in the \n accompanying figure, is illustrative. The city of Boston has \n a population of 620,000, but its metropolitan area is \n commonly defined to include 101 cities and towns with 4.5 \n million total residents. An organization known as a \n Metropolitan Planning Organization (MPO) that covers the \n territory of all the cities and towns in the region has been \n created to coordinate planning of major transportation \n investments. The primary purpose of the current study is to \n provide an overview of the ways in which systems of \n metropolitan transportation governance are organized in a \n six different countries in order that these systems might \n provide models for World Bank client countries currently \n developing institutions for managing urban transport \n problems. The best method for understanding how each of \n these systems operates is consulting the county case studies \n provided in the final section. This study is organized as \n follows. The first section presents an overview of several \n themes that run through the cases. In the subsequent \n sections, each case is reviewed individually.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".