Bibliographic record
Abstract
What makes a great city? Not a good city or a functional city but a great city. A city that people admire, learn from, and replicate. City planner and architect Alexander Garvin set out to answer this question by observing cities, largely in North America and Europe, with special attention to Paris, London, New York, and Vienna. For Garvin, greatness is not just about the most beautiful, convenient, or well-managed city; it isn’t even about any “city.” It is about what people who shape cities can do to make a city great. A great city is not an exquisite, completed artifact. It is a dynamic, constantly changing place that residents and their leaders can reshape to satisfy their demands. While this book does discuss the history, demographic composition, politics, economy, topography, history, layout, architecture, and planning of great cities, it is not about these aspects alone. Most importantly, it is about the interplay between people and public realm, and how they have interacted throughout history to create great cities. To open the book, Garvin explains that a great public realm attracts and retains the people who make a city great. He describes exactly what the term public realm means, its most important characteristics, as well as providing examples of when and how these characteristics work, or don’t. An entire chapter is devoted to a discussion of how particular components of the public realm (squares in London, parks in Minneapolis, and streets in Madrid) shape people’s daily lives. He concludes with a look at how twenty-first century initiatives in Paris, Houston, Atlanta, Brooklyn, and Toronto are making an already fine public realm even better—initiatives that demonstrate what other cities can do to improve. This volume will help readers understand that any city can be changed for the better and inspire entrepreneurs, public officials, and city residents to do it themselves.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".