Do New Economic Geography agglomeration shadows underlie current population dynamics across the urban hierarchy?
Bibliographic record
Abstract
Although the New Economic Geography (NEG) has been used extensively to formally explain the emergence of the American urban system, few studies investigate its success in explaining current population dynamics in a more established urban system. This study explores whether proximity to same–sized and higher–tiered urban centres affected the patterns of 1990–2006 US county population growth. Rather than casting NEG agglomeration shadows on nearby growth, larger urban centres generally appear to have positive growth effects for more proximate places of less than 250,000 people. However, there is some evidence the largest urban areas cast growth shadows on proximate medium–sized metropolitan areas and of spatial competition among small metropolitan areas. Resumen Aunque la Nueva Geografía Económica (NEG, por sus siglas en inglés) ha sido utilizada ampliamente para explicar de modo formal el surgimiento del sistema urbano estadounidense, pocos estudios investigan su éxito en explicar las dinámicas de población actuales en un sistema urbano más establecido. Este estudio explora la posibilidad de que centros urbanos del mismo tamaño y de elevada magnitud afectó a los patrones de crecimiento poblacional en condados de los EE.UU. en 1990-2006. En lugar de proyectar sombras de aglomeración de acuerdo a la NEG sobre el crecimiento cercano, los centros urbanos más grandes parecen tener en general efectos de crecimiento positivos en lugares cercanos de menos de 250,000 habitantes. Sin embargo, existen indicios de que las áreas urbanas más grandes proyectan sombras de crecimiento sobre áreas metropolitanas próximas de tamaño mediano y de competencia espacial entre áreas metropolitanas pequeñas.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".