Bibliographic record
Abstract
A large body of archaeological and anthropological research suggests that warfare is more common when societies are stratified. This is true for societies based on either sedentary foraging or agriculture. We argue that warfare in stratified societies does not require climatic or technological shocks, and results from competition among rival elites over land rent. In our model, elites recruit specialized warriors by offering booty in the event of victory, which may involve elevation to elite status. After each elite recruits an army, the rival elites must decide whether to attack, defend, or flee. We solve for the equilibrium at the combat stage as a function of army sizes, and use backward induction to solve for the equilibrium army sizes. If stratification is relatively low (the land rents are small relative to commoner food income), elites can sometimes win through intimidation without fighting an actual war. But if stratification is high, such equilibria disappear and the only outcome is a mixed-strategy equilbrium with a positive probability of open war. In either case, successful elites expand their territory. Fiscal constraints on the capacity of elites to recruit warriors can sometimes limit warfare, but do not prevent it entirely.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".