Bibliographic record
Abstract
This chapter constructs a theory about the origins of inequality. Our model involves a continuum of sites that have differing productivities with respect to food. All sites are initially open, and free mobility of agents across sites tends to equalize the food incomes of the agents. However, an organized group that is large enough relative to the land area of a site can establish property rights over that site and keep other agents from entering. As climate or technology improves, population densities grow, and over time the best sites become closed. This generates insider–outsider inequality, where different groups have different standards of living depending on the productivities of their sites. Eventually insiders at the best sites find it profitable to hire outsiders to work on their land, either by paying them a wage or requiring them to pay land rent. This gives elite–commoner inequality, or stratification. Class positions become hereditary. Technical progress makes commoners worse off in the long run because as regional population rises, more sites are closed. The sites that remain open are the least desirable. These predictions are consistent with archaeological evidence from southwest Asia, Europe, Polynesia, and the Channel Islands of California.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.011 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".