Big cities fuel inequality within and across generations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it