Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 isnât even about any âcity.â 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 donât. 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 peopleâs 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 betterâinitiatives 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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it