The Emergence of Cities and States
Bibliographic record
Abstract
This chapter reviews the literature on the origins of cities and states. We argue that purely agricultural societies are unlikely to have cities because population dispersal reduces travel costs for farmers and herders. But incentives for agglomeration could arise from the productivity of urban manufacturing, the need for collective defense, or cultural factors. We supplement our study of Mesopotamia with archaeological data on state formation in Egypt, the Indus Valley, China, Mesoamerica, and the Andes. All of these cases had highly productive food technologies, pre-existing stratification, and close links with urbanization. Based on our models in Chapters 6, 8, and 10, we suggest three pathways to a state. In the “property rights hypothesis,” improving food technology and long-run population growth lead to the creation of elite property rights over the best sites, a shrinking commons, and falling commoner wages. This eventually triggers urban manufacturing and city-state formation. In the “elite warfare hypothesis,” warfare among elites over land rents causes defensive agglomeration in cities and territorial expansion by successful elites. In the “environmental shift hypothesis,” commoner populations migrate toward refuge sites (often river valleys) controlled by local elites, again leading to falling commoner wages, urbanization, and state formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".