Bibliographic record
Abstract
These are remarkable books about the long origins of what had been our time, our twentieth-century liberal era. They reach back to the formative stage of New York's history to understand the institutions and people behind its web of governance and vast public sphere. David M. Scobey wrestles with the question of what touched off New York's spasm of civic improvement in the third quarter of the nineteenth century, when laborers turned the first earth for Central Park, steps were taken toward comprehensive planning, and towers rose for the great bridge that would unite a metropolis. Looking for a causal explanation, Scobey borrows the Marxist perspectives of the historian Henri Lefebvre and the geographer David Harvey. The era of improvements, he argues, was forced by the turmoil of uneven development, the lurching capitalism that brought the extremes of terminals and tenements. “Instead of mastery over time and space,” he writes, “the built environment embodied a propulsive drama of rupture and displacement” (p. 87). Beginning in the 1850s a coalition of urban intellectuals such as William Cullen Bryant, genteel reformers such as Frederick Law Olmsted, and enlightened businessmen such as Andrew H. Green came together to build the “tutelary” ground for the masses at Central Park, plan improvements along upper Broadway, and sketch entire city-scapes for the Bronx and Staten Island. For a brief time in the 1870s, Green became the overall coordinator of uptown development, as near to an American Haussmann as the city would have.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.221 | 0.093 |
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".