Wealth, fertility and adaptive behaviour in industrial populations
Bibliographic record
Abstract
The lack of association between wealth and fertility in contemporary industrialized populations has often been used to question the value of an evolutionary perspective on human behaviour. Here, we first present the history of this debate, and the evolutionary explanations for why wealth and fertility (the number of children) are decoupled in modern industrial settings. We suggest that the nature of the relationship between wealth and fertility remains an open question because of the multi-faceted nature of wealth, and because existing cross-sectional studies are ambiguous with respect to how material wealth and fertility are linked. A literature review of longitudinal studies on wealth and fertility shows that the majority of these report positive effects of wealth, although levels of fertility seem to fall below those that would maximize fitness. We emphasize that reproductive decision-making reflects a complex interplay between individual and societal factors that resists simple evolutionary interpretation, and highlight the role of economic insecurity in fertility decisions. We conclude by discussing whether the wealth-fertility relationship can inform us about the adaptiveness of modern fertility behaviour, and argue against simplistic claims regarding maladaptive behaviour in humans.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".