Lost in space: population growth in the American hinterlands and small cities
Bibliographic record
Abstract
The sources of urban agglomeration and the development of the urban system have been studied extensively. Despite the pivotal role of the hinterlands in theories of the development of the urban system, little attention has been paid to the effect of urban agglomeration in a developed, mature economy on growth in the hinterlands. Therefore, this study examines how proximity to urban agglomeration affects contemporary population growth (PopGr) in hinterland U.S. counties. Proximity to urban agglomeration is measured in terms of both distances to higher tiered areas in the urban hierarchy and proximity to market potential (MP). Particular attention is paid to whether periodic changes and trends in underlying conditions (e.g. technology or transport costs) have altered PopGr patterns in the hinterlands and small urban centers. Over the period 1950–2000, we find strong negative growth effects of distances to higher tiered urban areas, with significant, but lesser effects of distance to MP. Further, the costs of distance, if anything, appear to be increasing over time, consistent with a number of recent theories stressing the effect of new technology on the spatial distribution of activity in a mature urban system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".