Bibliographic record
Abstract
Why are American cities, suburbs, and towns so distinct? Compared to European cities, those in the United States are characterized by lower densities and greater distances; neat, geometric layouts; an abundance of green space; a greater level of social segregation reflected in space; and—perhaps most noticeably—a greater share of individual, single-family detached housing. In Zoned in the USA , Sonia A. Hirt argues that zoning laws are among the important but understudied reasons for the cross-continental differences.Hirt shows that rather than being imported from Europe, U.S. municipal zoning law was in fact an institution that quickly developed its own, distinctly American profile. A distinct spatial culture of individualism—founded on an ideal of separate, single-family residences apart from the dirt and turmoil of industrial and agricultural production—has driven much of municipal regulation, defined land-use, and, ultimately, shaped American life. Hirt explores municipal zoning from a comparative and international perspective, drawing on archival resources and contemporary land-use laws from England, Germany, France, Australia, Russia, Canada, and Japan to challenge assumptions about American cities and the laws that guide them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".