Big cities fuel inequality within and across generations
Bibliographic record
Abstract
Abstract Urbanization has long fueled a dual narrative: cities are heralded as sources of economic dynamism and wealth creation yet criticized for fostering inequality and a range of social challenges. This paper addresses this tension using a multidisciplinary approach, combining social sciences methods with satellite imagery-based spatial pattern analysis to study the US urban expansion over the past century. We examine the impact of physical urban spatial characteristics (size, population density, and connectedness) on equality of opportunity, measured through intergenerational mobility, as well as its association with levels of income, wealth, and social capital. Our findings confirm that contemporary cities, particularly population-dense and expansive ones, are indeed divisive forces—acting as centers for income and wealth generation but failing to deliver equal opportunities for economic mobility. Perhaps surprisingly, this polarizing dynamic is a recent phenomenon. In the past, the most urbanized regions performed well in terms of income creation and equality of opportunity. Our analysis supports the hypothesis that the mid-20th century marked a pivotal shift toward more unequal and less inclusive patterns of urban growth.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".