Revitalizing the City: Strategies to Contain Sprawl and Revive the Core
Bibliographic record
Abstract
Part 1. Urban Growth 1. A Perspective on Suburban Expansion and Metropolitan Development, Krishna M. Akundi 2. Is There a Need to Contain Growth? Elise Bright 3. The Challenges of Smart Growth: The San Diego Case, Nico Calavita, Roger Caves, and Kathleen Ferrier Part 2. Metropolitan Administration 4. The Role of County Governments in Metropolitan Administration: A Study of the St. Louis MSA, Mark Tranel 5. Regional Governance and Sustainability: The Case of Vancouver, Alan Artibise and John Meligrana 6. The Effect of Regional Smart Growth on Metropolitan Growth and Construction: A Preliminary Assessment, Arthur C. Nelson and Raymond J. Burby Part 3. Central City Redevelopment 7. Impacts of Building Code Enforcement on the Housing Industry, Raymond J. Burby 8. Citizen Reaction to Brownfield Redevelopment, Sabina Dietrick and Stephen Farber 9. Payments in Lieu of Taxes: A Revenue Generating Strategy for Central Cities, Pam Leland Part 4. Central City-Suburb Connection 10. Central City and Suburban Policy Choices, Victoria Basolo 11. Mixed-Income Housing, Alex Schwartz 12. Cyber-Cities, Jill Gross Part 5. Conclusion 13. Conclusion: Opportunities and Challenges for Containing Sprawl and Revitalizing the Core, Alan Artibise
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".