Federal Reserve Bank of St. Louis REVIEW Second Quarter 2014 147 Representative Neighborhoods of the United States
Bibliographic record
Abstract
R acial segregation is a striking trait of U.S. cities. Iceland, Weinberg, and Steinmetz(2002) report that 64 percent of the black population would have needed to changeresidence for all U.S. neighborhoods to become fully integrated in the year 2000. Income differences across neighborhoods have also been well documented. Wheeler and La Jeunesse (2007) report that between-neighborhood inequality in 2000 represented around 20 percent of overall annual household income inequality in Census data. The variation in housing prices across neighborhoods has also been the focus of a large literature.1 This article attempts to summarize the landscape of U.S. cities using a small number of representative neighborhoods. The motivation for this effort is twofold. On the one hand, a clear and concise characterization of the American urban landscape may be useful in the con-struction of theories involving neighborhood formation. On the other hand, a simple repre-sentation can be used to impose empirical discipline on quantitative models with a small number of locations. These types of models are important since they can address complex dynamic issues such as the interaction between neighborhood formation and human capital accumulation without becoming computationally infeasible (see, for example, Fernandez and Many metropolitan areas in the United States display substantial racial segregation and substantial variation in incomes and house prices across neighborhoods. To what extent can this variation be sum-
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.077 |
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".