Community renewal through municipal investment : a handbook for citizens and public officials
Bibliographic record
Abstract
Local officials are making investment decisions to enhance the quality of life in their communities and to improve economic development conditions. These new programs are not municipal give-away, or, as some call them, corporate welfare programs, but efforts to invest wisely in downtown areas and neighborhoods with the goal of revitalizing them, with the hope that business and commerce will follow. This work presents case studies from Atlanta, Baltimore, Baton Rouge, Berkeley, Boulder, Cambridge, Charleston, Chattanooga, Chesterfield County, Chicago, Cleveland, Denver, DuPont, Grand Forks, Hampton, Hartford, Hayward, Houston, Kansas City, Lake Worth, Little Rock, Madison, Minneapolis, Nashville, New Bedford, Newark, Oakland, Orlando, Petuluma, Portland, Saint Paul, Santa Monica, Seattle, Toronto, and Washington, D.C. The case study topics include streetscapes, public plazas, museums, libraries, cultural parks, walkways and greenways, major infrastructure improvements, transit and transportation enhancements and other works.
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 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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".