Bibliographic record
Abstract
Los Angeles, California, is generally considered the archetypal sprawling metropolis. Yet traditional measures equate sprawl with low population density, and Los Angeles is among the densest and thereby the least sprawling cities in the United States. How can this apparent paradox be explained? This paper argues that the answer lies in the fact that Los Angeles exhibits a comparatively even distribution of population throughout its urbanized area. As a result, the city suffers from many consequences of high population density, including extreme traffic congestion, poor air quality, and high housing prices, while offering its residents few benefits that typically accompany this density, including fast and effective public transit, vibrant street life, and tightly knit urban neighborhoods. The city's unique combination of high average population density with little differentiation in the distribution of population might best be characterized as dense sprawl, a condition that embodies the worst of urban and suburban worlds. This paper uses Gini coefficients to illustrate variation in population density and then considers a number of indicators–-most relating either to the provision of transportation infrastructure or to travel behavior–-that demonstrate the effects of low-variation population distribution on the quality of urban life in Los Angeles. This approach offers researchers, practitioners, and policy makers in Los Angeles and in smaller cities that are evolving in similar ways a useful and user-friendly tool for identifying, explaining, measuring, and addressing the most problematic aspects of sprawl.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".