Skinny Streets and Green Neighborhoods: Design for Environment and Community
Bibliographic record
Abstract
Review: Skinny Streets and Green Neighborhoods: Design for Environment and Community By Cynthia Girling and Ronald Kellett Reviewed by Kathy Piselli Atlanta Fulton Public Library, USA Cynthia Girling and Ronald Kellett. Skinny Streets and Green Neighborhoods: Design for Environment and Community. Washington, DC: Island Press, 2005. ISBN: 1-59726-028-2. ISBN 13:978-1-59726-028- 2 $60.00 trade cloth. ISBN:1-55963-337-9 ISBN 13:978-1-55963-337- 6 $35.00 trade paper. The oil embargo and energy crisis of 1973 trained a high-beam headlight on the wasteful way America had become accustomed to getting itself to work in urban areas. Though suburbs had been the major urban construction project since the end of World War II, good planning was rarely used in building them. The result was a combination of cookie-cutter architecture, poor land use, and automobile-dominated transportation networks resulting in sprawl, dramatic loss of green space, horrendous traffic situations for commuters, and even fines on some cities for Clean Air Act violations. More than a generation later, can it be said that anything has changed? In the majority of areas, the answer is, sadly, no. But there are exceptions. This book showcases examples of good contemporary thinking in urban planning. Focusing on the neighborhood rather than one building or region, these promote urban ecology and good environmental design. A preface provides concise but critical background, introducing neophytes to the lingo of urban architects, terms such as “gray”, “green”, and “infill”. In the same concise language it explains why neighborhoods should bother to incorporate environmental concerns into planning. One section profiles each case study with color illustrations. The case studies are a collection of developments located in large cities and suburban areas. One is in the eastern U.S., three in the Midwest, and six in the west; four are in Canada. All but two date from the late 1990s and the newest was initiated in 2005. Several are “brownfields”, built on former industrial sites. One has methane gas beneath it, a common problem when building on or near a wetlands or landfill. Many incorporate wetland restoration into their design. All make decisions about ratio of gray to green. In two other chapters, the issues of gray and green building are treated in more depth, using the case studies as illustrations. The chapter on gray
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".