Differentiating pathways of neighborhood change in 50 U.S. metropolitan areas
Bibliographic record
Abstract
Rapid transformations sweeping the United States over the past 50 years have necessitated a reassessment of longstanding theories on how the neighborhood change process has unfolded. This article builds upon recent methodological advancements aimed at understanding longitudinal dynamics by developing a workflow that blends the self-organizing map and a sequential alignment method to visualize pathways of change in a multivariate context. It identifies the predominant pathways in which neighborhoods have changed according to their racial, ethnic, socioeconomic and housing characteristics in the largest US metropolitan statistical areas from 1980 to 2010. The distribution of these pathways is subsequently examined between metropolitan statistical areas and the spatial clustering of these trajectories within cities is analyzed. Results reveal a white-flight type process, the establishment of a multiethnic neighborhood, densification of single-family neighborhoods, gentrification in relatively diverse neighborhoods, upgrading of white single family neighborhoods, and the most frequent pathway of all: no change. High-poverty minority and wealthy white neighborhoods are most spatially compact and expanding in a contiguous manner, while multiethnic neighborhoods are relatively dispersed. Six groups of metropolitan statistical areas are identified based upon the similarity of their neighborhood composition. Parallels are drawn between the formation of enduring high-poverty black neighborhoods in Northern and Midwestern cities and the emergence of clusters high-poverty Hispanic neighborhoods in Hispanic destination cities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".