Bibliographic record
Abstract
Abstract Much has been written about cities as engines of growth and prosperity. Cities have been centers of civilization since the beginning of history. A rich nation without cities is an impossibility. Yet, as this book explains, the central foundations of wealth and economic well-being are rooted in the attributes of nations and actions of national governments. If the nation does not work, nor will its cities. This book looks at the economy of cities through the lens of “The Ten Pillars of Urban Success,” covering a full range of policy concerns from top (i.e., sound macroeconomic management) to bottom (i.e., safe neighborhoods). Cities rich and poor around the world that are as different as New York, Vienna, Buenos Aires, and Port au Prince are examined. Urban success or failure almost always takes us back to the wise or unwise decisions of national and/or state governments. Urban success is about more than economics. Cities that have managed to produce livable urban environments for the majority of their citizens mirror the societies that spawned them. Similarly, cities that have failed are almost always signs of more deep-rooted failures. A socially cohesive city in a divided nation is an oxymoron. In the final chapter, the book proposes a critical look at America’s urban failures, its declining Rustbelt cities, and inner-city ghettos. Such failures should not have happened in the world’s richest nation. That they did is not only the sign of a deeper malaise, but also a warning to the wealthy urbanizing societies of tomorrow.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".