Explaining Below-Replacement Fertility and Increasing Childlessness in Wealthy Countries: Legacy Drive and the “Transmission Competition” Hypothesis
Bibliographic record
Abstract
We propose a novel evolutionary perspective for explaining why, in most wealthy countries, female fertility has recently dropped below replacement level, with an increasing incidence of childlessness. Our hypothesis is based on the proposition that throughout human evolution, behaviors that promoted gene transmission (offspring production), and hence fitness, have involved not just those associated with a strong “sex drive,” but also those associated with a strong “legacy drive”—the desire to “leave something of oneself for the future. Because of this intrinsic legacy drive, we argue, humans (and males, in particular) have been inherently vulnerable for “side-tracking” into other activities that promote “meme transmission” — i.e., activities perceived as providing a lasting legacy of “self through investment in career development, accumulation of wealth and status, and several other activities that have potential to impact on the thoughts and actions of others in both current and future generations. Humans engage in meme transmission, therefore, at the potential expense of time, energy, and resources for investing in gene transmission. Based on evolutionary arguments, we discuss why realized competition between gene transmission and meme transmission has emerged significantly only in recent human history, why meme transmission is presently winning out in wealthy countries — thus accounting for below-replacement fertility and increasing childlessness — and why natural selection can be expected in the near future to generate a significant shift in the fertility-promoting behaviors of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".