DP17475 How did the European marriage pattern persist?:social versus familial inheritance: England and Quebec, 1650-1850
Bibliographic record
Abstract
Eric Turkheimer famously stated as a Law, "All human behavioral traits are heritable." But this poses a puzzle for pre-industrial demographic systems, such as the European Marriage Pattern, where individuals made behavioral choices that limited fertility. Why were these behaviors not replaced over time with those that generated higher fertility? Some have argued the solution to this puzzle is that limited fertility in the first generation was actually maximal fertility in subsequent generations. But we show that there was no fertility penalty to future generations from higher fertility in the initial generation in both England and Quebec. Here we argue instead that the European Marriage Pattern survived for more than 500 years because, for pre-industrial fertility behavior, Turkheimer's Law does not hold. Even though at the social level fertility limiting behaviors transmitted strongly, there was scant familial inheritance of fertility choices. So fertility enhancing deviations did not get transmitted within families across generations, and the European Marriage Pattern could persist indefinitely. In the paper we show evidence consistent with horizontal as opposed to vertical transmission of fertility behaviors.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".