Bibliographic record
Abstract
Trust me, I read a lot of books on cities.This one is different.The Evolution of Great World Cities is one of the most truly original takes on cities and their economic development that I've read in quite a while.I met Chris Kennedy several years ago, via our colleague Eric Miller, who heads the University of Toronto's Cities Centre.I had only recently arrived in Toronto to lead a new think tank, the Martin Prosperity Institute, dedicated to studying what makes cities and regions succeed.In our first get-together, Kennedy outlined his ideas on the role of infrastructure -and by that I mean infrastructure broadly defined -in the growth and development of cities.He told me he had just finished a manuscript on this issue, and I asked if he would send it over to me.When I read the early drafts of what would become this book, I was blown away.Here's a work that builds on the insights of figures from Adam Smith to Jane Jacobs and considers the recent findings from civil engineering and physics to construct a new perspective on cities and their economic development.Stories from London to Detroit to Hong Kong to Toronto help illustrate the common threads that weave together great cities through the ages.Kennedy is a systems thinker in the best sense of that phrase: he's able to analyse and interpret the data and the on-the-ground dynamics, but he also sees the bigger picture and takes the longer view.His greatest contribution lies in showing the role that infrastructure plays in the growth and development of cities, especially the world's great cities.The economies of cities and their infrastructure systems are so closely intertwined that it is often difficult to tell where one ends and the other begins.Wise additions to a city's infrastructure lead to new growth, and smart growth provides the prosperity and impetus for fresh waves
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.594 | 0.596 |
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".