Political Centralization, Federalism, and Urban Development: Evidence from US and Canadian Capital Cities
Bibliographic record
Abstract
A growing empirical literature links political centralization with urban development. In this paper we present evidence showing how different patterns of political centralization in the United States and Canada affected urban agglomeration during the twentieth century, with a specific focus on the impact on the population of capital cities. Using data on Canadian and US cities and metropolitan areas, we find that the national capital effect on population grew over time in both countries but more so in the United States whereas the subnational (i.e., provincial or state) capital effect rose much more significantly in Canada than in the United States, controlling for other factors like geography and climate. We argue that these patterns in the national and subnational capital city effects reflect different trends in federalism in the two countries. In the United States, the Jeffersonian-Jacksonian tradition of states’ rights and localism was transformed into a more nationally centralized form of federalism during the Progressive Era, but states and localities continued to retain significant autonomy. In Canada, federalism came to favor provincial rights but not localism. We believe that that these diverging trends were driven by institutional differences that gave the various levels of governments in Canada and the United States different access to revenue sources.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".