Bibliographic record
Abstract
While many older American cities struggle to remain vibrant, New Brunswick has transformed itself, adapting to new forms of commerce and a changing population, and enjoying a renaissance that has led many experts to cite this New Jersey city as a model for urban redevelopment. Featuring more than 100 remarkable photographs and many maps, New Brunswick, New Jersey explores the history of the city since the seventeenth century, with an emphasis on the dramatic changes of the past few decades. Using oral histories, archival materials, census data, and surveys, authors David Listokin, Dorothea Berkhout, and James W. Hughes illuminate the decision-making and planning process that led to New Brunswick’s dramatic revitalization, describing the major redevelopment projects that demonstrate the city’s success in capitalizing on funding opportunities. These projects include the momentous decision of Johnson & Johnson to build its world headquarters in the city, the growth of a theater district, the expansion of Rutgers University into the downtown area, and the destruction and rebuilding of public housing. But while the authors highlight the positive effects of the transformation, they also explore the often heated controversies about demolishing older neighborhoods and ask whether new building benefits residents. Shining a light on both the successes and failures in downtown revitalization, they underscore the lessons to be learned for national urban policy, highlighting the value of partnerships, unwavering commitment, and local leadership. Today, New Brunswick’s skyline has been dramatically altered by new office buildings, residential towers, medical complexes, and popular cultural centers. This engaging volume explores the challenges facing urban America, while also providing a specific case study of a city’s quest to raise its economic fortunes and retool its economy to changing needs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.410 | 0.146 |
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".