Bibliographic record
Abstract
Population dynamics are central to any theory of economic prehistory. This chapter explains the Malthusian framework widely used by economists. We rely on these ideas throughout the book. The exposition is graphical and should be accessible to non-economists. We define a production function and the average and marginal products of labor. With fixed natural resources and a fixed technology, food per person decreases as the population of a geographic area increases. Decreasing food per person tends to lower fertility and raise mortality. These demographic effects yield an equilibrium with a stable long-run population. If technology improves, food per person rises at a given population level. In the short run this raises the standard of living for the existing population, but in the long run, population growth brings the standard of living back down to its previous level. The main implication is that in the long run, technological innovation or a better climate raises population but not living standards. We discuss the relationship of these ideas to the concepts of migration, carrying capacity, population density, and population pressure. We conclude with a review of empirical evidence supporting the relevance of Malthusian models for pre-industrial societies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".